FIFA 20 Game analysis

Project Name: PRCP-1004-Fifa20

Y Vagesh(vagesh129@gmail.com),

Data Collected From:

The data is about related to the online video game FIFA. The data originates from the Canadian video game company Electronic Arts (EA) publishes a series of football simulation video game annually under the banner of EA Sports FIFA. These video games simulate the real-world playing styles and techniques used in football, and provide players with a sense of pride and accomplishment in terms of managing a team of virtual football plyers.

The series began using the official FIFA license in 1993 and has since produced yearly instalments, the latest being EA Sports FIFA 20. Each annual release occurs around the end of August or September, with a playable demo released two-three weeks earlier. The numbering scheme followed by the franchise relates to the shortening of the upcoming football season of that particular year; for example, the FIFA 20 game includes the players and clubs participating in the 2019-20 football season in the real life.

This is usually determined by two ratings given to the players-player overall and player potential. Player overall gives the rating of the player in the current scenario in real life, whereas player potential tries to determine the potential to which the player can reach over time.

Domain Analysis

1. Sofifa id :

ID of the player in fifa 20

2. url :

url to fifa 20 player profile

3. short Name :

short Name or the name used by a perticular player in game

4. long_name :

full name of th player

5. Age :

Age of the player

6. birth_date :

the date of the birth of given player

7. height_cm :

height of the player in centimeters

9. weight_kg :

weight of the player in kilograms

10. nationality :

nationality of the player

11. club :

The club player plays for

12. overall :

overall attribute of a player

13. potential :

potential attribute of a player

14. value_eur :

value in EUR of the player

15. wage_eur :

wage in EUR of the player

16. player_positions :

positions of the player

17. preferred_foot :

preferred foot of the player

18. international_reputation :

international reputation attribute

19. weak_foot :

weak foot of the player

20. skill_moves :

quality moves attribute

21. work_rate :

productivity attribute

22. body_type :

type of body

23.real_face:

Only Generic Faces that updated to Real Faces,what the person seems to be.

24.release_clause_eur:

Some players will have a release clause. This is like a trump card that lets you bypass the selling team and go straight to contract negotiations with the player, as long as you're willing to pay it. If the release clause is close to his value, it can be a great way to cut out a stubborn selling team.

25.player_tags:

A label attached to players for the purpose of identification or to give other information.Showing great potential.

26.team_position

This involves drafting a team position-by-position from many of the best. arrangement of a team on its field of play during a game.

27.Team_jersey_number

The number is more reflective of a player's role on the team. It is a tradition from early "association football".

28. loaned_from:

Loan players are limited use items that can boost the overall performance of your team. In this Loan Players Guide for FIFA 20 Ultimate Team.team has the player been loaned

29. joined:

Join to bring in contact, connect, or bring or put together that the player joined to team or club.

30 .contract_valid_until:

Have a valid ... Agreement” Register their valid EA Account for eligibility and accept the Official Rules

31. nation_position:

A position where the team stands in the world.

32.nation_jersey_number:

Number assigned to the team of particular country

33. pace

Pace (PAC) is a Player Attribute in FIFA that determines a player's speed in walking and running.

34. shooting:

To take a shot hitting to ball in an attempt to score a goal.

35.Passing:

Passing is the primary method of distributing the ball in a game of FIFA. It might appear to be easy on the surface, but a massive skill gap exists between players who know how to press the pass button and players who have gained mastery of FIFA 20’s passing mechanics.

36. Dribbling:

Dribbling is crucial when playing a match in FIFA 20. Without carrying the ball and passing your opponent, you won't be able to score in FIFA 20. The better you are at dribbling, the easier you can control the game. The Active Touch system in FIFA 20 will help you to control the ball a way better.

37.Defending:

Being able to switch to your preferred player while defending will allow you to cover open spaces and make interceptions more efficiently. Imagine a scenario where an opponent's player is running down the left flank, you could manually switch to your left-back to track your opponent's run and win the ball back.

38. physic

Physical (PHY), also knows as Physicality, is a Player Attribute in FIFA that represents the physical and body status of a player.

39.gk_diving:

The goalkeeper has relatively simple controls in the penalty kick shootout. To block penalties you can move the goalkeeper from side to side with the left stick. You can then use the right stick to dive in any direction. Goalkeepers can also do gestures with the face buttons on the controller.

40. gk_kicking:

Placing the ball down, take one large stride 'straight' back. Mark that point either mentally or with a stud mark, then take a few smaller paces back. Don't go off the pitch. Then take one large sidestep to the left or right, depending which foot you kick with

41. gk_reflexes:

Reflex saves are attributed to a keeper's alertness. Take penalties saves as an example. Prior to making their dive, goalkeepers stand upright, arms out, on their toes, and poised to make a quick move. ... The keeper has to react very quickly to get the ball to safety

42. gk_speed:

Speed makes only a small difference for pulling the keeper for 1v1 and if your moving them it will make a small difference to but not noticeable since you only move them a few steps. The 2nd most important this is their distribution if your playing to win.

43. gk_positioning:

Shows Position of Goalkeepers The goalkeeper's main job is to stop the other team from scoring by catching, palming or punching the ball from shots, headers and crosses. Unlike their teammates, goalkeepers typically remain in and around their own penalty area for most of the game.

44. player_traits:

Traits are used to provide players with unique skills and play styles in the game that match their real-life counterparts. Each Trait impacts some aspect of the player’s behaviour on the pitch or, in some cases, off of it.

45. 'attacking_crossing':

This attribute measures how accurately the player crosses the ball during both normal running and free kick set pieces.

46. 'attacking_finishing':

Finishing is the accuracy of shots using foot, inside the penalty area. a great finisher would have this force push it towards the goal, or when it would’ve normally gone wide the ball would at least hit the post.

47.'attacking_heading_accuracy':

This stats measures the heading accuracy of the player for either a pass or a shot. In fact, this stats does two things: affects your player’s ability to get their head on the ball, then how accurate that header is going to be. It applies to headed passes as well as headers at goal.

48. 'attacking_short_passing':

A pass of the ball between teammates too close together to allow an opponent to get between them. A short side foot pass enables a team to quickly pass a ball and help maintain possession. The side foot pass provides accuracy. This attribute ranks how well a player performs a short / ground pass to his teammate. In other words, it determines a player’s accuracy and speed of passing over a short distance.

49. 'attacking_volleys':

A volley is usually a shot hit first time without the ball touching the ground. This attribute measures the accuracy and power of volleys at goal. It affects the technique and accuracy of shots taken while the ball is in the air. This tends to be coupled with the balance trait if he is not fully facing the goal. Volleys stat is responsible for some of the most beautiful goals.

50. 'skill_dribbling':

Dribbling is the player’s ability to carry the ball and past an opponent. A higher value means the player will be able to keep better possession of the ball whilst dribbling because he will keep the ball closer, making it harder for the opponent to win it off of him.

51. 'skill_curve':

Player Attribute in FIFA that determines a player's ability to curve the ball when passing and shooting. The higher the value the more curve/curl the player is capable of putting on the ballIt’s a good attribute to have for any set piece takers; curling corners can be a nightmare to defend against, and curling free-kicks can be tough to save.

52. 'skill_fk_accuracy':

Free Kick is a FIFA attribute used to measures the player’s accuracy for taking Free Kicks. The higher the value the better the accuracy of a direct free kick on goal. More player scores during a free kick, the higher will be his points or percentage in terms of free kick accuracy. Ideally, you should pick a free-kick taker who has a good score for free kick accuracy and curve

53. 'skill_long_passing':

This stat is used to classify how well a player performs a long pass in the air to his teammate. It doesn’t affect long ground passes. This also determines how quickly the ball gets to him – the higher the score, the faster and more accurate the pass will be.

54. 'skill_ball_control':

Getting and keeping the ball under control is an important skill a football player has to learn and in response to this, an essential skill.

55. 'movement_acceleration':

Sprint speed measures how fast the player runs while at top speed.

56. 'movement_sprint_speed’:

A game of football consists of a series of sprints strategically performed over 90 minutes. These sprints involve lots of short, rapid bursts of acceleration.

57. 'movement_agility':

Agility measures how agile the player is while moving or turning. In other words, how fast and graceful a player is able to control the ball. Players with high agility can perform acrobatic shots or clearances, and agility also affects dribbling ability. If you’re a player who likes to run with the ball, agility is one of the stats to keep an eye on.

58. 'movement_reactions':

Is a Player Attribute in FIFA that determines the acting speed of a player in response to the situations happening around them. Reactions measures how quickly a player responds to a situation happening around him.

59. 'movement_balance':

Balance attribute is the ability to maintain balance after a physical challenge. This is another attribute that influences a player’s dribbling skill and, more generally, how responsive the player you’re controlling feels. If you have high stats for agility and balance then you’ll move fluidly and you’ll be able to get in or out of tight spots. Also, even if you have 95+ for acceleration and sprint speed, your player could feel sluggish and a little unresponsive if they lack agility and balance

60. 'power_shot_power':

Shot Power evaluates how hard the player hits the ball when taking a shot at goal. It is the amount of power a player can put into a shot while still keeping it accurate. It affects how fast the ball travels when struck from any distance. A player with a low shot power can still hit the ball hard into the back of the net but the longer you hold B/O the more likely he is to miss.

61. 'power_jumping':

Jumping is the player’s ability and quality for jumping from the surface for header.The higher the value is, the higher the player can jump.

62. 'power_stamina':

Stamina determines the rate at which a player will tire during a game. It evaluates how tired your player gets as the match approaches half time or full time.

63. 'power_strength':

Strength is about the quality or state of being physically strong. The higher the value, the more likely the player will win a physical challenge.

64.'power_long_shots':

This attribute measures the accuracy of shots from outside the penalty area. This is a great attribute for midfielders to have.

65. 'mentality_aggression':

The aggression level of a player measures the frequency and the aggression of jostling, tackling and slide tackling. It is the attribute which determines the player’s power of will or commitment to a match.

66. 'mentality_interceptions'

Interceptions is a Player Attribute in FIFA that determines a player's capability to intercept the ball - to catch the opposing team's passes.

67.Mentality Positioning :

Positioning (POS) is a Player Attribute in FIFA that determines that how well a player is able to perform the positioning on the field as a player or on the goal line as a goalkeeper.

68. Mentality Vision:

Vision ranks the player's awareness of the position of his teammates & opponents around him. It is the attribute that increases (or reduces) the possibilities of a successful long pass. When we're playing looking at the screen we usually have an above view of the game, just like when we're watching football on TV.

69. Mentality Penalty:

The ideal penalty will fill two or two-and-a-half bars, which gives it more than enough power to ensure the goalkeeper cannot reach it. Timed finishing has also been integrated into FIFA 21's penalties, meaning that you can improve the accuracy of your spot kicks by pressing the shoot button at the moment of impact.

70. Mentality Composure:

COMPOSURE This attribute determines at what distance the playerwith the ball starts feeling the pressure from the opponent. This then affects the chances of theplayer making an error when he shoots, passes and crosses. The higher the value, the better the player performs when under pressure from an opponent.

71. Defending Marking:

Marking is the ability to track and defend an opposing player. In otherwords, it is your player's ability to stay close to an opposing attacker and stop him getting to a cross/pass from a teammate. Also contributes to tracking runs.

72. Defending standing tackle:-

The best way to defend is to jockey (L2/LT) to move into positionand then use standing tackle (Circle/X) to win the ball. A badly-timed standing tackle can leave you exposed, so only use it when close to the attacker.

73. Defending sliding tackle:

In FIFA 20, you can power up your standing and sliding tackles by holding the tackle button for longer. This allows you to lunge further with your tackles, but if you power up too much, you will either miss the ball or cause a foul. Two bars should be the maximum power for any tackle.

74. Goalkeeping diving:

The goalkeeper has relatively simple controls in the penalty kick shootout. To block penalties you can move the goalkeeper from side to side with the left stick. You can then use the right stick to dive in any direction. Goalkeepers can also do gestures with the face buttons on the controller

75. Goalkeeping handling:

Handling is an exclusive goalkeeper attribute used to measures how cleanly he catches the ball and does he hold on to it. In other words, it is the frequency that the keeper catches the ball rather than parrying it and whether or not he holds onto it.

76. Goalkeeping kicking:

To perform a drop kick, press the button responsible for the shooting, and select the direction and strength of the play.

77. Goalkeeping positioning:

Goalkeeper (GK) The Goalkeeper (or GK for short) is the mostspecialized position in football. A goalkeeper's job is mainly defensive: to guard the team's goal from being breached (to not let the other team score). Goalkeeper is the only position defined in the Laws of the Game.

78. Goalkeeping reflexes:

Professional goalkeepers regularly work on their reflexes in training. Reflex training drills are designed to simulate match situations where keepers are required to act fast — such as shots from point blank range, second rebounds, or attempts made through a crowd of players.

79. LS – Left Striker:

In soccer, the abbreviation “LS” means Left Striker. The left striker(LS) is positioned on the left-hand side of the forward players near the opposition’s goal. The role of the left striker(LS) is to score goals for the team when given the opportunity and to create opportunities from the left-hand side of the soccer field.

80.ST – Striker

In soccer, the abbreviation “ST” means Striker. The striker(ST) on a soccer team is the player positioned nearest to the opposition’s goal. The role of the striker(ST) is to score goals for the team and be the focal point for any passes made when the team is in attack.

81.RS – Right Striker

In soccer, the abbreviation “RS” means Right Striker. The right striker(RS) is positioned on the right-hand side of the forward players near the opposition’s goal. The role of the right striker(RS) is to score goals for the team when given the opportunity and to create opportunities from the right-hand side of the soccer field.

82.LW – Left Winger

In soccer, the abbreviation “LW” means Left Winger. The left-winger(LW) is positioned on the left-wing of the team, meaning the left-hand side. The role of the left-winger(LW) is to cover the entire left side of the soccer field supporting both the offense and defense.

83.LF – Left Forward

In soccer, the abbreviation “LF” means Left Forward. The left forward(LF) is positioned on the left-hand side of the team’s attacking line. The role of the left forward(LF) is to contribute to the goals scored by their team as well as creating goal-scoring opportunities for other players on the team.

84.CF – Center Forward

In soccer, the abbreviation “CF” means Center Forward. The Center Forward(CF) is the player positioned furthest forward on a team and operates closer to the opposition’s goal than any other player on the team. The role of the Center Forward(CF) is to score goals for the team.

85.RF – Right Forward

In soccer, the abbreviation “RF” means Right Forward. The right forward(RF) is positioned furthest forward in a soccer team on the right-hand side of the soccer field. The role of the right forward is to work with the other offensive players on the team to create goal-scoring opportunities and score goals for their team.

86.RW – Right Winger

In soccer, the abbreviation “RW” means Right Winger. The right-winger(RW) is positioned on the right-wing of the team, meaning the right-hand side. The role of the right-winger(RW) is to cover the entire right side of the soccer field supporting both the offense and defense.

87.LAM – Left Attacking Midfielder

In soccer, the abbreviation “LAM” means Left Attacking Midfielder. The left Attacking midfielder(LAM) is positioned on the left-hand side of the soccer field and has the responsibility of supporting both the defense and forward players any time the soccer ball is on the left side of the field. The left Attacking midfielder(LAM) is often one of the fastest players on a team and is skilled at crossing the ball.

88. CAM – Central Attacking Midfielder

In soccer, the abbreviation “CAM” means Central Attacking Midfielder. The Central Attacking Midfielder, or CAM, on a team, is positioned in the center of the soccer field just behind the forwards. Their role is to assist and support the forwards on their team in an attack on the opposition goal.

89. RAM – Right Attacking Midfielder

In soccer, the abbreviation “RAM” means Right Attacking Midfielder. The right Attacking midfielder(RM) is positioned on the right-hand side of the midfield. The role of the right attacking midfielder(RAM) is to link the team’s defense and attacking players on the right-hand side of the field and support both the offense and defense.

90. LM – Left Midfielder

In soccer, the abbreviation “LM” means Left Midfielder. The left midfielder(LM) is positioned on the left-hand side of the soccer field and has the responsibility of supporting both the defense and forward players any time the soccer ball is on the left side of the field. The left midfielder(LM) is often one of the fastest players on a team and is skilled at crossing the ball.

91.LCM –Left Central Midfield

Who has a role balanced between attack and defence. Is positioned on the left side and closer to the touchlines of the pitch. Left midfielders are usually left-footed.

92.CM – Central Midfield

The abbreviation “CM” means Central Midfield. The soccer, the player who plays central midfield(CM) is positioned at the center of the team between the defense and attack. They have a multi-faceted role which includes supporting the defense and the forwards. They are often referred to as the engine room of the team.

93.RCM – Right Centra Midfielder

Is positioned on the Right side and closer to the touchlines of the pitch. Left midfielders are usually left-footed.They literally strengthen the team in its core and are characterized by constantly being in position to receive and pass the ball. In addition to a number of defensive tasks, they lead the offensive gameplay after receiving the ball from the center backs.

94.RM – Right Midfielder

In soccer, the abbreviation “RM” means Right Midfielder. The right midfielder(RM) is positioned on the right-hand side of the midfield. The role of the right midfielder(RM) is to link the team’s defense and attacking players on the right-hand side of the field and support both the offense and defense.

95.LWB – Left Wing Back

In soccer, the abbreviation “LWB” means Left Wing Back. The left wing back(LWB) is positioned on the left-hand side of a soccer team’s defense. The role of the left wing back(LWB) is to defend the left-hand side of the soccer field from the opposition as well as supporting the teams attacking players by moving up the field when the soccer ball is in the attacking half of the field.

96.LDM – Left Defensive Midfielder

97.CDM – Central Defensive Midfielder

In soccer, the abbreviation “CDM” means central defensive midfielder. The central defensive midfielder(CDM) is positioned at the center of the soccer field, just in front of their team’s defenders, and is positioned the furthest back of the midfielders. The role of the central defensive midfielder (CDM) is to protect the defense and be a link between the defense and midfield.

98.RDM – Right Defensive Midfielder

They are positioned on either side of the centre-back. They defend against opposing wingers in the flanks who try to drive the ball past them or pass to other attackers. They do not traditionally support attacking plays.

99.RWB – Right Wing Back

In soccer, the abbreviation “RWB” means Right Wing Back. The right wing back(RWB) is positioned on the right-hand side of a soccer team’s defense. The role of the right wing back(RWB) is to defend the right-hand side of the soccer field from the opposition as well as supporting the teams attacking players by moving up the field when the soccer ball is in the attacking half of the field.

100.LB – Left Back

In soccer, the abbreviation “LB” means Left Back. The left back(LB) is part of the team’s defense and positioned on the left side of the soccer field. The role of the left back(LB) is to defend the left-hand side of the soccer field and prevent the opposition from scoring a goal.

101.LCB – Left Center Back

In soccer, the abbreviation “LCB” means Left Center Back. The left-center back(LCB) is positioned on the left side of the defense on a soccer team. The role of the left-center back(LCB) is to defend the left-hand side of the soccer field and prevent any opposition players, or the soccer ball, from getting past them.

102.CB – Center Back

The meaning of the abbreviation “CB” is Center Back. In soccer, the center back(CB) is positioned in the center of the field just in front of the goalkeeper at the center of the team’s defense. The role of the center back(CB) is to protect the team’s goal and prevent any of the opposition’s players, or the soccer, getting past them.

103.RCB – Right Center Back

In soccer, the abbreviation “RCB” means Right Center Back. The right center back(RCB) is positioned on the right-hand side of the center of defense. The right center back(RCB) often plays alongside a left center back(LCB) and they cover the center of the defense between them. The role of the right center back(RCB) is to prevent the opposition from scoring a goal.

104. RB – Right Back

In soccer, the abbreviation “RB” means Right Back. The right back(RB) is part of the team’s defense and positioned on the right-hand side of the soccer field. The role of the right back(RB) is to defend the right-hand side of the soccer field and prevent the opposition from scoring a goal.

Business case:

To Design the clustering algorithm to categorize similar players using the traits and their different skills in the field.

Type of problem :

For the given data we Perform Clustering Task.

Installing required libraries

!pip install plotly-express
!pip install plotly.express

Requirement already satisfied: plotly-express in c:\users\vagesh\anaconda3\lib\site-packages (0.4.1)
Requirement already satisfied: numpy>=1.11 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly-express) (1.19.2)
Requirement already satisfied: scipy>=0.18 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly-express) (1.5.2)
Requirement already satisfied: pandas>=0.20.0 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly-express) (1.2.3)
Requirement already satisfied: plotly>=4.1.0 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly-express) (4.14.3)
Requirement already satisfied: statsmodels>=0.9.0 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly-express) (0.12.0)
Requirement already satisfied: patsy>=0.5 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly-express) (0.5.1)
Requirement already satisfied: python-dateutil>=2.7.3 in c:\users\vagesh\anaconda3\lib\site-packages (from pandas>=0.20.0->plotly-express) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in c:\users\vagesh\anaconda3\lib\site-packages (from pandas>=0.20.0->plotly-express) (2020.1)
Requirement already satisfied: retrying>=1.3.3 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly>=4.1.0->plotly-express) (1.3.3)
Requirement already satisfied: six in c:\users\vagesh\anaconda3\lib\site-packages (from plotly>=4.1.0->plotly-express) (1.15.0)
Requirement already satisfied: plotly.express in c:\users\vagesh\anaconda3\lib\site-packages (0.4.1)
Requirement already satisfied: statsmodels>=0.9.0 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly.express) (0.12.0)
Requirement already satisfied: patsy>=0.5 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly.express) (0.5.1)
Requirement already satisfied: scipy>=0.18 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly.express) (1.5.2)
Requirement already satisfied: numpy>=1.11 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly.express) (1.19.2)
Requirement already satisfied: plotly>=4.1.0 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly.express) (4.14.3)
Requirement already satisfied: pandas>=0.20.0 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly.express) (1.2.3)
Requirement already satisfied: six in c:\users\vagesh\anaconda3\lib\site-packages (from patsy>=0.5->plotly.express) (1.15.0)
Requirement already satisfied: retrying>=1.3.3 in c:\users\vagesh\anaconda3\lib\site-packages (from plotly>=4.1.0->plotly.express) (1.3.3)
Requirement already satisfied: pytz>=2017.3 in c:\users\vagesh\anaconda3\lib\site-packages (from pandas>=0.20.0->plotly.express) (2020.1)
Requirement already satisfied: python-dateutil>=2.7.3 in c:\users\vagesh\anaconda3\lib\site-packages (from pandas>=0.20.0->plotly.express) (2.8.1)

Importing the Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns 

import plotly.graph_objs as go
import plotly.express as px

import warnings
warnings.filterwarnings('ignore')

pd.set_option('display.max_columns',None)

Extracting the Data

data=pd.read_csv('players_20.csv')
data.head(1)

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	158023	https://sofifa.com/player/158023/lionel-messi/...	L. Messi	Lionel Andrés Messi Cuccittini	32	1987-06-24	170	72	Argentina	FC Barcelona	94	94	95500000	565000	RW, CF, ST	Left	5	4	4	Medium/Low	Messi	Yes	195800000.0	#Dribbler, #Distance Shooter, #Crosser, #FK Sp...	RW	10.0	NaN	2004-07-01	2021.0	NaN	NaN	87.0	92.0	92.0	96.0	39.0	66.0	NaN	NaN	NaN	NaN	NaN	NaN	Beat Offside Trap, Argues with Officials, Earl...	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89+2	89+2	89+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	92+2	87+2	87+2	87+2	92+2	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2

Basic Checks

#Shape of the data
data.shape

(18278, 104)

The data Consists of 18278 rows and 104 columns

Head and Tail of the Data

#retreving the first five rows using head()
data.head()

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	158023	https://sofifa.com/player/158023/lionel-messi/...	L. Messi	Lionel Andrés Messi Cuccittini	32	1987-06-24	170	72	Argentina	FC Barcelona	94	94	95500000	565000	RW, CF, ST	Left	5	4	4	Medium/Low	Messi	Yes	195800000.0	#Dribbler, #Distance Shooter, #Crosser, #FK Sp...	RW	10.0	NaN	2004-07-01	2021.0	NaN	NaN	87.0	92.0	92.0	96.0	39.0	66.0	NaN	NaN	NaN	NaN	NaN	NaN	Beat Offside Trap, Argues with Officials, Earl...	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89+2	89+2	89+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	92+2	87+2	87+2	87+2	92+2	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2
1	20801	https://sofifa.com/player/20801/c-ronaldo-dos-...	Cristiano Ronaldo	Cristiano Ronaldo dos Santos Aveiro	34	1985-02-05	187	83	Portugal	Juventus	93	93	58500000	405000	ST, LW	Right	5	4	5	High/Low	C. Ronaldo	Yes	96500000.0	#Speedster, #Dribbler, #Distance Shooter, #Acr...	LW	7.0	NaN	2018-07-10	2022.0	LS	7.0	90.0	93.0	82.0	89.0	35.0	78.0	NaN	NaN	NaN	NaN	NaN	NaN	Long Throw-in, Selfish, Argues with Officials,...	84	94	89	83	87	89	81	76	77	92	89	91	87	96	71	95	95	85	78	93	63	29	95	82	85	95	28	32	24	7	11	15	14	11	91+3	91+3	91+3	89+3	90+3	90+3	90+3	89+3	88+3	88+3	88+3	88+3	81+3	81+3	81+3	88+3	65+3	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3
2	190871	https://sofifa.com/player/190871/neymar-da-sil...	Neymar Jr	Neymar da Silva Santos Junior	27	1992-02-05	175	68	Brazil	Paris Saint-Germain	92	92	105500000	290000	LW, CAM	Right	5	5	5	High/Medium	Neymar	Yes	195200000.0	#Speedster, #Dribbler, #Playmaker  , #Crosser,...	CAM	10.0	NaN	2017-08-03	2022.0	LW	10.0	91.0	85.0	87.0	95.0	32.0	58.0	NaN	NaN	NaN	NaN	NaN	NaN	Power Free-Kick, Injury Free, Selfish, Early C...	87	87	62	87	87	96	88	87	81	95	94	89	96	92	84	80	61	81	49	84	51	36	87	90	90	94	27	26	29	9	9	15	15	11	84+3	84+3	84+3	90+3	89+3	89+3	89+3	90+3	90+3	90+3	90+3	89+3	82+3	82+3	82+3	89+3	66+3	61+3	61+3	61+3	66+3	61+3	46+3	46+3	46+3	61+3
3	200389	https://sofifa.com/player/200389/jan-oblak/20/...	J. Oblak	Jan Oblak	26	1993-01-07	188	87	Slovenia	Atlético Madrid	91	93	77500000	125000	GK	Right	3	3	1	Medium/Medium	Normal	Yes	164700000.0	NaN	GK	13.0	NaN	2014-07-16	2023.0	GK	1.0	NaN	NaN	NaN	NaN	NaN	NaN	87.0	92.0	78.0	89.0	52.0	90.0	Flair, Acrobatic Clearance	13	11	15	43	13	12	13	14	40	30	43	60	67	88	49	59	78	41	78	12	34	19	11	65	11	68	27	12	18	87	92	78	90	89	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	183277	https://sofifa.com/player/183277/eden-hazard/2...	E. Hazard	Eden Hazard	28	1991-01-07	175	74	Belgium	Real Madrid	91	91	90000000	470000	LW, CF	Right	4	4	4	High/Medium	Normal	Yes	184500000.0	#Speedster, #Dribbler, #Acrobat	LW	7.0	NaN	2019-07-01	2024.0	LF	10.0	91.0	83.0	86.0	94.0	35.0	66.0	NaN	NaN	NaN	NaN	NaN	NaN	Beat Offside Trap, Selfish, Finesse Shot, Spee...	81	84	61	89	83	95	83	79	83	94	94	88	95	90	94	82	56	84	63	80	54	41	87	89	88	91	34	27	22	11	12	6	8	8	83+3	83+3	83+3	89+3	88+3	88+3	88+3	89+3	89+3	89+3	89+3	89+3	83+3	83+3	83+3	89+3	66+3	63+3	63+3	63+3	66+3	61+3	49+3	49+3	49+3	61+3

#retrieving the lost five row using tail()
data.tail()

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
18273	245006	https://sofifa.com/player/245006/shuai-shao/20...	Shao Shuai	邵帅	22	1997-03-10	186	79	China PR	Beijing Renhe FC	48	56	40000	2000	CB	Right	1	3	2	Medium/High	Normal	No	70000.0	NaN	RES	36.0	NaN	2018-07-13	2019.0	NaN	NaN	57.0	23.0	28.0	33.0	47.0	51.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	21	17	40	35	27	24	23	21	28	35	56	58	45	40	73	36	70	60	47	16	46	48	28	25	39	41	45	50	52	5	5	13	5	14	32+2	32+2	32+2	31+2	31+2	31+2	31+2	31+2	31+2	31+2	31+2	33+2	33+2	33+2	33+2	33+2	43+2	42+2	42+2	42+2	43+2	45+2	46+2	46+2	46+2	45+2
18274	250995	https://sofifa.com/player/250995/mingjie-xiao/...	Xiao Mingjie	Mingjie Xiao	22	1997-01-01	177	66	China PR	Shanghai SIPG FC	48	56	40000	2000	CB	Right	1	2	2	Medium/Medium	Normal	No	72000.0	NaN	SUB	31.0	NaN	2019-02-01	2022.0	NaN	NaN	58.0	24.0	33.0	35.0	48.0	48.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	24	20	42	43	28	32	24	29	39	31	55	61	43	41	76	33	72	55	44	20	42	49	23	25	37	35	42	53	57	13	6	14	11	9	33+2	33+2	33+2	33+2	32+2	32+2	32+2	33+2	33+2	33+2	33+2	35+2	36+2	36+2	36+2	35+2	44+2	43+2	43+2	43+2	44+2	46+2	47+2	47+2	47+2	46+2
18275	252332	https://sofifa.com/player/252332/wei-zhang/20/...	Zhang Wei	张威	19	2000-05-16	186	75	China PR	Hebei China Fortune FC	48	56	40000	1000	CM	Right	1	2	2	Low/Medium	Lean	No	70000.0	NaN	SUB	38.0	NaN	2019-01-01	2019.0	NaN	NaN	54.0	35.0	44.0	45.0	48.0	51.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	32	33	49	53	32	40	32	32	55	49	55	54	52	52	57	48	60	50	51	26	50	45	38	38	36	39	46	52	46	7	8	10	6	14	43+2	43+2	43+2	43+2	43+2	43+2	43+2	43+2	44+2	44+2	44+2	45+2	46+2	46+2	46+2	45+2	47+2	49+2	49+2	49+2	47+2	47+2	49+2	49+2	49+2	47+2
18276	251110	https://sofifa.com/player/251110/haijian-wang/...	Wang Haijian	汪海健	18	2000-08-02	185	74	China PR	Shanghai Greenland Shenhua FC	48	54	40000	1000	CM	Right	1	2	2	Low/Low	Lean	No	70000.0	NaN	SUB	33.0	NaN	2019-02-01	2022.0	NaN	NaN	59.0	35.0	47.0	47.0	45.0	52.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	39	34	47	54	28	42	37	39	48	49	55	63	55	54	59	46	61	42	55	28	57	49	31	48	36	40	39	44	54	14	9	13	13	13	43+2	43+2	43+2	45+2	44+2	44+2	44+2	45+2	46+2	46+2	46+2	46+2	46+2	46+2	46+2	46+2	48+2	48+2	48+2	48+2	48+2	48+2	49+2	49+2	49+2	48+2
18277	233449	https://sofifa.com/player/233449/ximing-pan/20...	Pan Ximing	潘喜明	26	1993-01-11	182	78	China PR	Hebei China Fortune FC	48	51	40000	2000	CM	Right	1	3	2	Medium/Medium	Normal	No	NaN	NaN	SUB	37.0	Tianjin TEDA FC	NaN	2019.0	NaN	NaN	60.0	32.0	51.0	45.0	47.0	55.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	44	30	48	58	25	40	34	35	52	46	54	64	52	49	71	41	64	40	61	30	54	47	29	52	38	42	52	42	48	8	12	14	9	10	42+2	42+2	42+2	44+2	43+2	43+2	43+2	44+2	46+2	46+2	46+2	46+2	46+2	46+2	46+2	46+2	48+2	49+2	49+2	49+2	48+2	48+2	50+2	50+2	50+2	48+2

Description of the data

##Describing the numerical data
data.describe().T

	count	mean	std	min	25%	50%	75%	max
sofifa_id	18278.0	219738.864482	27960.200461	768.0	204445.5	226165.0	240795.75	252905.0
age	18278.0	25.283291	4.656964	16.0	22.0	25.0	29.00	42.0
height_cm	18278.0	181.362184	6.756961	156.0	177.0	181.0	186.00	205.0
weight_kg	18278.0	75.276343	7.047744	50.0	70.0	75.0	80.00	110.0
overall	18278.0	66.244994	6.949953	48.0	62.0	66.0	71.00	94.0
...	...	...	...	...	...	...	...	...
goalkeeping_diving	18278.0	16.572765	17.738069	1.0	8.0	11.0	14.00	90.0
goalkeeping_handling	18278.0	16.354853	16.996925	1.0	8.0	11.0	14.00	92.0
goalkeeping_kicking	18278.0	16.212934	16.613665	1.0	8.0	11.0	14.00	93.0
goalkeeping_positioning	18278.0	16.368038	17.136497	1.0	8.0	11.0	14.00	91.0
goalkeeping_reflexes	18278.0	16.709924	18.038125	1.0	8.0	11.0	14.00	92.0

61 rows × 8 columns

#Describing the categorical data
data.describe(include='O').T

	count	unique	top	freq
player_url	18278	18278	https://sofifa.com/player/246848/boubacar-fofa...	1
short_name	18278	17354	J. Rodríguez	11
long_name	18278	18218	Liam Kelly	3
dob	18278	6142	1992-02-29	113
nationality	18278	162	England	1667
club	18278	698	Southampton	33
player_positions	18278	643	CB	2322
preferred_foot	18278	2	Right	13960
work_rate	18278	9	Medium/Medium	9875
body_type	18278	10	Normal	10750
real_face	18278	2	No	16310
player_tags	1499	83	#Strength	514
team_position	18038	29	SUB	7820
loaned_from	1048	316	Atalanta	17
joined	16990	1760	2019-07-01	1465
nation_position	1126	26	SUB	587
player_traits	7566	921	Early Crosser	501
ls	16242	94	61+2	725
st	16242	94	61+2	725
rs	16242	94	61+2	725
lw	16242	107	63+2	736
lf	16242	103	63+2	727
cf	16242	103	63+2	727
rf	16242	103	63+2	727
rw	16242	107	63+2	736
lam	16242	103	61+2	748
cam	16242	103	61+2	748
ram	16242	103	61+2	748
lm	16242	100	61+2	809
lcm	16242	88	58+2	775
cm	16242	88	58+2	775
rcm	16242	88	58+2	775
rm	16242	100	61+2	809
lwb	16242	98	59+2	682
ldm	16242	98	59+2	636
cdm	16242	98	59+2	636
rdm	16242	98	59+2	636
rwb	16242	98	59+2	682
lb	16242	96	61+2	667
lcb	16242	110	63+2	621
cb	16242	110	63+2	621
rcb	16242	110	63+2	621
rb	16242	96	61+2	667

Information of about the data:

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18278 entries, 0 to 18277
Columns: 104 entries, sofifa_id to rb
dtypes: float64(16), int64(45), object(43)
memory usage: 14.5+ MB

WE have 104 columns and 18278 rows in the data. The of their datatypes are:

• 16 are float64 type ,
• 45 are int64 type and
• 43 are object type of columns.

Representation of the all Column names:

#104 column names 

for i in data.columns:
    print(i)

sofifa_id
player_url
short_name
long_name
age
dob
height_cm
weight_kg
nationality
club
overall
potential
value_eur
wage_eur
player_positions
preferred_foot
international_reputation
weak_foot
skill_moves
work_rate
body_type
real_face
release_clause_eur
player_tags
team_position
team_jersey_number
loaned_from
joined
contract_valid_until
nation_position
nation_jersey_number
pace
shooting
passing
dribbling
defending
physic
gk_diving
gk_handling
gk_kicking
gk_reflexes
gk_speed
gk_positioning
player_traits
attacking_crossing
attacking_finishing
attacking_heading_accuracy
attacking_short_passing
attacking_volleys
skill_dribbling
skill_curve
skill_fk_accuracy
skill_long_passing
skill_ball_control
movement_acceleration
movement_sprint_speed
movement_agility
movement_reactions
movement_balance
power_shot_power
power_jumping
power_stamina
power_strength
power_long_shots
mentality_aggression
mentality_interceptions
mentality_positioning
mentality_vision
mentality_penalties
mentality_composure
defending_marking
defending_standing_tackle
defending_sliding_tackle
goalkeeping_diving
goalkeeping_handling
goalkeeping_kicking
goalkeeping_positioning
goalkeeping_reflexes
ls
st
rs
lw
lf
cf
rf
rw
lam
cam
ram
lm
lcm
cm
rcm
rm
lwb
ldm
cdm
rdm
rwb
lb
lcb
cb
rcb
rb

Separating the Numerical and Categorical columns from the data

cat_columns=[n for n in data.columns if data[n].dtypes=='object']

len(cat_columns)

43

Categorical columns are 43

numerical=[n for n in data.columns if data[n].dtypes!='object']

len(numerical)

61

numerical columns are 61

Missing values

data[data.columns[data.isna().any()]].head(1)

	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	195800000.0	#Dribbler, #Distance Shooter, #Crosser, #FK Sp...	RW	10.0	NaN	2004-07-01	2021.0	NaN	NaN	87.0	92.0	92.0	96.0	39.0	66.0	NaN	NaN	NaN	NaN	NaN	NaN	Beat Offside Trap, Argues with Officials, Earl...	89+2	89+2	89+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	92+2	87+2	87+2	87+2	92+2	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2

tot_null_columns=data.columns[data.isnull().any()]
a=data[tot_null_columns].isnull().sum()
a.head(49)

release_clause_eur       1298
player_tags             16779
team_position             240
team_jersey_number        240
loaned_from             17230
joined                   1288
contract_valid_until      240
nation_position         17152
nation_jersey_number    17152
pace                     2036
shooting                 2036
passing                  2036
dribbling                2036
defending                2036
physic                   2036
gk_diving               16242
gk_handling             16242
gk_kicking              16242
gk_reflexes             16242
gk_speed                16242
gk_positioning          16242
player_traits           10712
ls                       2036
st                       2036
rs                       2036
lw                       2036
lf                       2036
cf                       2036
rf                       2036
rw                       2036
lam                      2036
cam                      2036
ram                      2036
lm                       2036
lcm                      2036
cm                       2036
rcm                      2036
rm                       2036
lwb                      2036
ldm                      2036
cdm                      2036
rdm                      2036
rwb                      2036
lb                       2036
lcb                      2036
cb                       2036
rcb                      2036
rb                       2036
dtype: int64

Number of columns are consists missing values in the Dataset

len(a)

48

There are 48 columns are consists of missing values.

Percentage of Missing values in each columns

missing_data = a.sort_values(ascending=False)
missing_data = missing_data.reset_index(drop = False)
missing_data = missing_data.rename(columns={"index": "Columns", 0:"missing_Values"})
missing_data['percentage'] = (missing_data['missing_Values']/len(data))*100
missing_data.head(5)

	Columns	missing_Values	percentage
0	loaned_from	17230	94.266331
1	nation_jersey_number	17152	93.839589
2	nation_position	17152	93.839589
3	player_tags	16779	91.798884
4	gk_positioning	16242	88.860926

missing values below 11 percent in the data

missing_data[missing_data['percentage']<=11]

	Columns	missing_Values	percentage
43	release_clause_eur	1298	7.101433
44	joined	1288	7.046723
45	contract_valid_until	240	1.313054
46	team_jersey_number	240	1.313054
47	team_position	240	1.313054

Missing values above 11 percent in the data

import plotly.express as px
sample = missing_data[missing_data['percentage']>11]
fig = px.pie(sample, names='Columns', values='percentage',
             color_discrete_sequence=px.colors.sequential.Plasma_r,
             title='More than 11 Percentage of Missing values Columns',height=700)
fig.update_traces(textposition='inside', textinfo='label')
fig.update_layout(paper_bgcolor='rgba(0,0,0,0)',
                  plot_bgcolor='rgba(1,1,1,1)',
                  font=dict(family='Cambria, monospace', size=12, color='#000000'))

fig.show()

#categorical columns are assign to the data2 variable
data1=data[['player_url','short_name','long_name','dob','nationality','club','player_positions','preferred_foot','work_rate','body_type','real_face','player_tags','team_position','loaned_from',
 'joined','nation_position','player_traits','ls','st','rs','lw','lf','cf','rf','rw','lam','cam','ram','lm','lcm','cm','rcm','rm','lwb','ldm','cdm','rdm','rwb','lb','lcb','cb','rcb','rb']]

#missing values in numerical columns
null_columns=data1.columns[data1.isnull().any()]
cat_null=data1[null_columns].isnull().sum()
len(cat_null)

32

There are 32 categorical columns consits of missing values.

#Numerical columns are assign to the data1 variable
data2=data[['sofifa_id', 'age','height_cm','weight_kg','overall','potential','value_eur','wage_eur','international_reputation','weak_foot', 'skill_moves','release_clause_eur','team_jersey_number','contract_valid_until','nation_jersey_number',
 'pace','shooting','passing','dribbling','defending','physic','gk_diving','gk_handling','gk_kicking','gk_reflexes','gk_speed','gk_positioning','attacking_crossing','attacking_finishing','attacking_heading_accuracy','attacking_short_passing',
 'attacking_volleys','skill_dribbling','skill_curve','skill_fk_accuracy','skill_long_passing','skill_ball_control','movement_acceleration','movement_sprint_speed','movement_agility','movement_reactions','movement_balance','power_shot_power',
 'power_jumping','power_stamina','power_strength','power_long_shots','mentality_aggression','mentality_interceptions','mentality_positioning', 'mentality_vision','mentality_penalties','mentality_composure','defending_marking','defending_standing_tackle',
 'defending_sliding_tackle','goalkeeping_diving','goalkeeping_handling','goalkeeping_kicking','goalkeeping_positioning', 'goalkeeping_reflexes']]

#missing values in numerical columns
nul_columns=data2.columns[data2.isnull().any()]
num_null=data2[nul_columns].isnull().sum()
len(num_null)

16

There is 16 numerical columns are the consists of missing values.

Exploratory Data Analysis

AGE:

import plotly.graph_objs as go
import plotly.express as px

plt.figure(figsize=(14,5))
plt.title('Age Distribution FIFA 20',fontsize=20)
sns.distplot(a=data['age'], kde=True, bins=20,color='indigo')
plt.axvline(x=np.mean(data['age']),c='tomato',label='Mean Age of All Players')
plt.legend()
plt.show()
print('-----------------------------------------------------------------------------------------------------------------','\n',)
print('Insights:')
print('average age of all players:        ',round(np.mean(data['age'])), ) #data['age'].mean()
print('The age of the youngest player is: ',data['age'].min())
print('The age of the oldest player is:   ',np.max(data['age']))
print('Youngest Players:')
print('Total No of youngest players in the data:',len(data.loc[data['age'] == data['age'].min()]))
print('Oldest Players:')
print('Total No of oldest players in the data:', len(data.loc[data['age'] == data['age'].max()]))

----------------------------------------------------------------------------------------------------------------- 

Insights:
average age of all players:         25
The age of the youngest player is:  16
The age of the oldest player is:    42
Youngest Players:
Total No of youngest players in the data: 12
Oldest Players:
Total No of oldest players in the data: 2

Finding 15 youngest Players from the FIFA 20 dataset

data.sort_values('age', ascending = True)[['short_name', 'age', 'club', 'nationality']].head(15).style.background_gradient('viridis')

	short_name	age	club	nationality
18171	J. Starbuck	16	Grimsby Town	England
4764	A. Hložek	16	Sparta Praha	Czech Republic
18243	E. Sartorius	16	Lincoln City	England
17827	D. Burns	16	St. Patrick's Athletic	Northern Ireland
17614	D. Obbekjær	16	Odense Boldklub	Denmark
17782	S. Anderson	16	Colorado Rapids	United States
18242	R. Wikberg	16	Östersunds FK	Sweden
18031	O. Stefánsson	16	IFK Norrköping	Iceland
14626	A. Velasco	16	Independiente	Argentina
12160	S. Esposito	16	Inter	Italy
12158	E. Millot	16	AS Monaco	France
6630	Fábio Silva	16	FC Porto	Portugal
13979	N. Wood	17	Middlesbrough	England
13981	M. Olise	17	Reading	France
13987	F. Tolomello	17	Trapani	Italy

Finding 15 eldest players from the FIFA 20 dataset

data.sort_values('age', ascending = False)[['short_name', 'age', 'club', 'nationality']].head(15).style.background_gradient('inferno')

	short_name	age	club	nationality
13003	H. Sulaimani	42	Al Ahli	Saudi Arabia
11832	C. Muñoz	42	CD Universidad de Concepción	Argentina
200	G. Buffon	41	Juventus	Italy
3706	C. Lucchetti	41	Atlético Tucumán	Argentina
10665	F. Kippe	41	Lillestrøm SK	Norway
868	Hilton	41	Montpellier HSC	Brazil
2965	Cifuentes	40	Cádiz CF	Spain
15812	M. Gurski	40	SpVgg Unterhaching	Germany
8359	F. Cubero	40	Vélez Sarsfield	Argentina
2963	D. Dainelli	40	Livorno	Italy
14527	D. Bulman	40	Crawley Town	England
6235	J. Gillet	40	Standard de Liège	Belgium
10702	M. Caranta	40	Club Atlético Talleres	Argentina
7230	Lee Dong Gook	40	Jeonbuk Hyundai Motors	Korea Republic
1880	C. Pizarro	40	SV Werder Bremen	Peru

Height in centimeters:

plt.figure(figsize=(18,8))
plt.title('Height(in centimeters) Distribution FIFA 20', fontsize=20)
sns.distplot(a=data['height_cm'], kde=True, bins=20,color='aquamarine')
plt.axvline(x=np.mean(data['height_cm']),c='orange',label='Mean Height of All Players')
plt.legend()
plt.show()
print('-----------------------------------------------------------------------------------------------------------------------------','\n',)
print('Insights:')
print('average height of all players:         ',round(np.mean(data['height_cm'])),"cm's") 
print('The height of the shortest player is:  ',data['height_cm'].min(),"cm's")
print('The height of the tallest player is:   ',np.max(data['height_cm']),"cm's")
print('shortestst players:')
print('Total No of shortestst players in the data:',len(data.loc[data['height_cm'] == data['height_cm'].min()]))
print('Tallest Players:')
print('Total No of tallest players in the data:', len(data.loc[data['height_cm'] == data['height_cm'].max()]))

----------------------------------------------------------------------------------------------------------------------------- 

Insights:
average height of all players:          181 cm's
The height of the shortest player is:   156 cm's
The height of the tallest player is:    205 cm's
shortestst players:
Total No of shortestst players in the data: 1
Tallest Players:
Total No of tallest players in the data: 1

data.loc[data['height_cm'] == data['height_cm'].min()]

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
4829	237994	https://sofifa.com/player/237994/nahuel-barrio...	N. Barrios	Cristian Nahuel Barrios	21	1998-05-07	156	58	Argentina	San Lorenzo de Almagro	70	80	2900000	8000	LM, CAM, LW	Right	1	3	3	High/Low	Normal	No	4900000.0	NaN	RW	28.0	NaN	2017-01-01	2022.0	NaN	NaN	76.0	63.0	67.0	72.0	40.0	38.0	NaN	NaN	NaN	NaN	NaN	NaN	Finesse Shot, Crowd Favourite	63	63	47	73	51	73	63	45	65	70	84	70	70	65	94	65	63	51	28	64	42	26	66	67	57	65	44	40	51	13	8	7	12	6	63+2	63+2	63+2	69+2	68+2	68+2	68+2	69+2	69+2	69+2	69+2	68+2	63+2	63+2	63+2	68+2	56+2	52+2	52+2	52+2	56+2	54+2	44+2	44+2	44+2	54+2

N. Barrios is the shortest person in the data

data.loc[data['height_cm'] == data['height_cm'].max()]

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
9208	199321	https://sofifa.com/player/199321/tomas-holy/20...	T. Holý	Tomáš Holý	27	1991-12-10	205	102	Czech Republic	Ipswich Town	66	70	575000	3000	GK	Right	1	3	1	Medium/Medium	Normal	No	1000000.0	NaN	GK	1.0	NaN	2019-07-01	2021.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	71.0	60.0	56.0	69.0	28.0	65.0	Swerve Pass	13	18	18	18	20	16	20	14	29	19	22	33	35	58	41	42	48	19	60	16	21	15	9	30	23	43	16	25	25	71	60	56	65	69	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

T. Holý is the tallest players in the data

plt.figure(figsize=(10,5))
sns.regplot(x="age", y="height_cm", data=data)
print("Showing Relations between Age and Height ")

Showing Relations between Age and Height 

weight in KG's

plt.figure(figsize=(18,8))
plt.title('Weight(in Kgs) Distribution FIFA 20', fontsize=20)
sns.distplot(a=data['weight_kg'], kde=True, bins=20,color='goldenrod')
plt.axvline(x=np.mean(data['weight_kg']),c='green',label='Mean Weight of All Players')
plt.legend()
plt.show()
print('-----------------------------------------------------------------------------------------------------------------','\n',)
print('Insights:')
print('average weight of all players:         ',round(np.mean(data['weight_kg'])),"kg's") 
print('The  lowest weight is:                 ',data['weight_kg'].min(),"kg's")
print('The highest weight player is:          ',np.max(data['weight_kg']),"kg's")
print('lowest weight players:-----')
print('Total No of lowest weight  players in the data:',len(data.loc[data['weight_kg'] == data['weight_kg'].min()]))
print('Heighest weight Players:-----------')
print('Total No of highest weight players in the data:', len(data.loc[data['weight_kg'] == data['weight_kg'].max()]))

----------------------------------------------------------------------------------------------------------------- 

Insights:
average weight of all players:          75 kg's
The  lowest weight is:                  50 kg's
The highest weight player is:           110 kg's
lowest weight players:-----
Total No of lowest weight  players in the data: 1
Heighest weight Players:-----------
Total No of highest weight players in the data: 1

data.loc[data['weight_kg'] == data['weight_kg'].min()]

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
13070	235600	https://sofifa.com/player/235600/bandar-al-mut...	B. Al Mutairi	Bandar Al Mutairi	29	1990-03-14	168	50	Saudi Arabia	Al Fayha	63	63	300000	4000	LWB	Left	1	3	2	Medium/Medium	Lean	No	495000.0	NaN	LWB	3.0	NaN	2019-07-06	2022.0	NaN	NaN	82.0	28.0	52.0	58.0	56.0	64.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	61	22	41	56	31	55	57	38	45	56	81	83	76	54	72	34	70	86	56	29	55	54	51	43	37	53	58	61	56	7	6	13	8	9	46+2	46+2	46+2	54+2	50+2	50+2	50+2	54+2	51+2	51+2	51+2	57+2	52+2	52+2	52+2	57+2	62+2	56+2	56+2	56+2	62+2	61+2	56+2	56+2	56+2	61+2

The lowest weighted player in the weight column is 'B. Al Mutairi'

data.loc[data['weight_kg'] == data['weight_kg'].max()]

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
10693	156321	https://sofifa.com/player/156321/adebayo-akinf...	A. Akinfenwa	Adebayo Akinfenwa	37	1982-05-10	178	110	England	Wycombe Wanderers	65	65	190000	2000	ST	Right	1	3	2	Low/Low	Akinfenwa	Yes	333000.0	#Strength	ST	20.0	NaN	2016-07-10	2020.0	NaN	NaN	43.0	63.0	54.0	56.0	35.0	81.0	NaN	NaN	NaN	NaN	NaN	NaN	Diver, Long Passer (CPU AI Only)	44	64	72	60	59	52	50	42	51	67	37	48	35	61	71	70	49	69	97	54	66	17	68	61	64	67	32	40	24	14	7	15	6	16	64+2	64+2	64+2	56+2	60+2	60+2	60+2	56+2	58+2	58+2	58+2	56+2	57+2	57+2	57+2	56+2	44+2	49+2	49+2	49+2	44+2	43+2	48+2	48+2	48+2	43+2

The highest weighted player in the weight column is 'A. Akinfenwa'

plt.figure(figsize=(10,5))
sns.regplot(x="age", y="weight_kg", data=data)
print("Showing Relations between Age and weight_kg ")

Showing Relations between Age and weight_kg 

Relationship between weight and height with age

plt.figure(figsize=(30,10))
sns.relplot(x=data.weight_kg,y=data.height_cm,hue=data.age,sizes=(20, 200),legend="full",palette="deep",kind='scatter')
plt.title('Relationship between Weight and Height with age', fontsize=12)
plt.show()
print('Insight:')
print('When height increase automatic weight is also increase with different age players')

<Figure size 2160x720 with 0 Axes>

Insight:
When height increase automatic weight is also increase with different age players

Nationality

plt.figure(figsize=(20,8))
data.nationality.value_counts()[:50].plot(kind='bar',color = 'lightgreen')
plt.title('Players from different countries present in FIFA-2021')
plt.xlabel('Nationality')
plt.ylabel('Count')
plt.show()
print('Insights:')
print("1. Most of the players are belonging to England from top 50")
print("2. Very few player are belonging to Canada from the top 50")

Insights:
1. Most of the players are belonging to England from top 50
2. Very few player are belonging to Canada from the top 50

print('No of countries in the nationality column is: ',data.nationality.nunique())

No of countries in the nationality column is:  162

Club

plt.figure(figsize=(20,8))
data.club.value_counts()[:50].plot(kind='bar',color = 'lightgreen')
plt.title('Players from different clubs present in FIFA-2020')
plt.xlabel('Nationality')
plt.ylabel('Count')
plt.show()
print('Insights:')
print('Top 49 clbs are consists of the 33 players in each club')

Insights:
Top 49 clbs are consists of the 33 players in each club

print('No of clubs inthe club column is :',data.club.nunique())

No of clubs inthe club column is : 698

Overall

plt.figure(figsize=(14, 7))
sns.distplot(data['overall'],bins=20,color='green')
plt.title("Overall Rating")
plt.axvline(x=np.mean(data['overall']),c='orange',label='Mean overall of All Players')
plt.legend()
plt.show()
print('-----------------------------------------------------------------------------------------------------------------','\n',)
print('Insights:')
print('average overall rating of all players:         ',round(np.mean(data['overall'])),"overall rating") 
print('The  lowest overall rating is:                 ',data['overall'].min(),"overall rating")
print('The highest overall rating is:                  ',np.max(data['overall']),"overall rating")
print('lowest overall players:-----')
print('Total No of lowest overall  players in the data:',len(data.loc[data['overall'] == data['overall'].min()]),'players')
print('highest overall rating Players:-----------')
print('Total No of highest overall rating players in the data:', len(data.loc[data['overall'] == data['overall'].max()]),'player')

----------------------------------------------------------------------------------------------------------------- 

Insights:
average overall rating of all players:          66 overall rating
The  lowest overall rating is:                  48 overall rating
The highest overall rating is:                   94 overall rating
lowest overall players:-----
Total No of lowest overall  players in the data: 43 players
highest overall rating Players:-----------
Total No of highest overall rating players in the data: 1 player

Top 10 players rating

top_overall=data.sort_values("overall", axis = 0, 
                                  ascending = False)[['short_name','nationality', 'club', 'overall', 'potential', 'value_eur']]
top_overall.head(10)

	short_name	nationality	club	overall	potential	value_eur
0	L. Messi	Argentina	FC Barcelona	94	94	95500000
1	Cristiano Ronaldo	Portugal	Juventus	93	93	58500000
2	Neymar Jr	Brazil	Paris Saint-Germain	92	92	105500000
3	J. Oblak	Slovenia	Atlético Madrid	91	93	77500000
4	E. Hazard	Belgium	Real Madrid	91	91	90000000
5	K. De Bruyne	Belgium	Manchester City	91	91	90000000
6	M. ter Stegen	Germany	FC Barcelona	90	93	67500000
7	V. van Dijk	Netherlands	Liverpool	90	91	78000000
8	L. Modrić	Croatia	Real Madrid	90	90	45000000
9	M. Salah	Egypt	Liverpool	90	90	80500000

fig = px.pie(top_overall.head(10), names='club',
             title='Percentage of Clubs in top 10 players on the basis of rating ',color_discrete_sequence=px.colors.sequential.Plasma_r, )
fig.show()

fig = px.pie(top_overall.head(10), names='nationality',
             title='Percentage of Nations in top 10 players on the basis of rating')
fig.show()

potential

plt.figure(figsize=(14, 7))
sns.distplot(data['potential'],bins=20,color='pink')
plt.title("potentiality of the players")
plt.axvline(x=np.mean(data['potential']),c='orange',label='Mean potential of All Players')
plt.legend()
plt.show()
print('-----------------------------------------------------------------------------------------------------------------','\n',)
print('Insights:')
print('average potentiality  of all players:         ',round(np.mean(data['potential'])),"potentiality") 
print('The  lowest potentiality  is:                 ',data['potential'].min(),"potentiality")
print('The highest potentiality  is:                  ',np.max(data['potential']),"potentiality")
print('lowest potentiality players:-----')
print('Total No of lowest potentiality  players in the data:',len(data.loc[data['potential'] == data['potential'].min()]),'players')
print('highest potentiality  Players:-----------')
print('Total No of highest potentiality  players in the data:', len(data.loc[data['potential'] == data['potential'].max()]),'player')

----------------------------------------------------------------------------------------------------------------- 

Insights:
average potentiality  of all players:          72 potentiality
The  lowest potentiality  is:                  49 potentiality
The highest potentiality  is:                   95 potentiality
lowest potentiality players:-----
Total No of lowest potentiality  players in the data: 2 players
highest potentiality  Players:-----------
Total No of highest potentiality  players in the data: 1 player

top_potentiality=data.sort_values("potential", axis = 0, 
                                  ascending = False)[['short_name','nationality', 'club', 'overall', 'potential', 'value_eur']]
top_potentiality.head(10)

	short_name	nationality	club	overall	potential	value_eur
10	K. Mbappé	France	Paris Saint-Germain	89	95	93500000
0	L. Messi	Argentina	FC Barcelona	94	94	95500000
6	M. ter Stegen	Germany	FC Barcelona	90	93	67500000
73	M. de Ligt	Netherlands	Juventus	85	93	50000000
1	Cristiano Ronaldo	Portugal	Juventus	93	93	58500000
411	João Félix	Portugal	Atlético Madrid	80	93	28000000
3	J. Oblak	Slovenia	Atlético Madrid	91	93	77500000
74	G. Donnarumma	Italy	Milan	85	92	41500000
108	J. Sancho	England	Borussia Dortmund	84	92	44500000
109	K. Havertz	Germany	Bayer 04 Leverkusen	84	92	46000000

fig = px.pie(top_potentiality.head(10), names='club',
             title='Percentage of Clubs in top 10 players on the basis of potentiality',color_discrete_sequence=px.colors.sequential.Plasma_r, )
fig.show()

fig = px.pie(top_potentiality.head(10), names='nationality',
             title='Percentage of Nations in top 10 players on the basis of potentiality')
fig.show()

value_eur

plt.figure(figsize=(14, 7))
sns.distplot(data['value_eur'],color='maroon')
plt.title("value_eur of the players")
plt.axvline(x=np.mean(data['value_eur']),c='gold',label='Mean potential of All Players')
plt.legend()
plt.show()
print('-----------------------------------------------------------------------------------------------------------------','\n',)
print('Insights:')
print('average value_eur  of all players:         ',round(np.mean(data['value_eur'])),"value_eur") 
print('The  lowest value_eur  is:                 ',data['value_eur'].min(),"value_eur")
print('The highest value_eur  is:                  ',np.max(data['value_eur']),"value_eur")
print('lowest value_eur players:-----')
print('Total No of lowest value_eur  players in the data:',len(data.loc[data['value_eur'] == data['value_eur'].min()]),'players')
print('highest value_eur  Players:-----------')
print('Total No of highest value_eur  players in the data:', len(data.loc[data['value_eur'] == data['value_eur'].max()]),'player')

----------------------------------------------------------------------------------------------------------------- 

Insights:
average value_eur  of all players:          2484038 value_eur
The  lowest value_eur  is:                  0 value_eur
The highest value_eur  is:                   105500000 value_eur
lowest value_eur players:-----
Total No of lowest value_eur  players in the data: 250 players
highest value_eur  Players:-----------
Total No of highest value_eur  players in the data: 1 player

sns.scatterplot(y=data.value_eur,x=data.overall)
plt.title('Relationship between value_eur and overall')
plt.show()
print('Whenever overall increases than automatically value_eur also increases')

Whenever overall increases than automatically value_eur also increases

plt.title('Relationship between value_eur and potential')
sns.scatterplot(y=data.value_eur,x=data.potential)
plt.show()
print('whenever potential increases than automatically value_eur also increases')

whenever potential increases than automatically value_eur also increases

preferred_foot:

data.preferred_foot.value_counts()

Right    13960
Left      4318
Name: preferred_foot, dtype: int64

plt.figure(figsize=(14, 7))
colors=['wheat','tan']
plt.title('percentage of prefered foot of players',fontsize=20,loc='right')
plt.pie(data.preferred_foot.value_counts(),labels=['Right','Left'],startangle=90,autopct='%1.0f%%',colors=colors,)
plt.show()
print('-----------------------------------------------------------------')
print('Insights:')
print("percentage of people are prefering to the right feet")

-----------------------------------------------------------------
Insights:
percentage of people are prefering to the right feet

print("The average overall  of preferred Right foot", data.loc[data['preferred_foot'] == 'Right']['overall'].mean())
print("The average overall  of preferred Left foot", data.loc[data['preferred_foot'] == 'Left']['overall'].mean())

The average overall  of preferred Right foot 66.11174785100286
The average overall  of preferred Left foot 66.67577582213988

Preferred foot doesn't have a significant impact on the overall.

Top 10 Left Footed Football Players

data[data['preferred_foot'] == 'Left'][['short_name', 'age', 'club', 'nationality']].head(10).style.background_gradient('magma')

	short_name	age	club	nationality
0	L. Messi	32	FC Barcelona	Argentina
9	M. Salah	27	Liverpool	Egypt
16	G. Chiellini	34	Juventus	Italy
22	A. Griezmann	28	FC Barcelona	France
23	P. Dybala	25	Juventus	Argentina
25	Ederson	25	Manchester City	Brazil
28	T. Courtois	27	Real Madrid	Belgium
32	H. Lloris	32	Tottenham Hotspur	France
33	David Silva	33	Manchester City	Spain
40	A. Laporte	25	Manchester City	France

Top 10 Right Footed Football Players

data[data['preferred_foot'] == 'Right'][['short_name', 'age', 'club', 'nationality']].head(10).style.background_gradient('viridis')

	short_name	age	club	nationality
1	Cristiano Ronaldo	34	Juventus	Portugal
2	Neymar Jr	27	Paris Saint-Germain	Brazil
3	J. Oblak	26	Atlético Madrid	Slovenia
4	E. Hazard	28	Real Madrid	Belgium
5	K. De Bruyne	28	Manchester City	Belgium
6	M. ter Stegen	27	FC Barcelona	Germany
7	V. van Dijk	27	Liverpool	Netherlands
8	L. Modrić	33	Real Madrid	Croatia
10	K. Mbappé	20	Paris Saint-Germain	France
11	K. Koulibaly	28	Napoli	Senegal

International reputation:

data.international_reputation.value_counts()

1    16794
2     1140
3      292
4       46
5        6
Name: international_reputation, dtype: int64

sns.countplot(data.international_reputation)
plt.show()
print('----------------------------------------')
print('Insights:')
print('92% of players international_reputation is "1" ')

----------------------------------------
Insights:
92% of players international_reputation is "1" 

len(data.loc[data['international_reputation'] == 5])

6

These 6 players (L. Messi, Cristiano Ronaldo, Neymar Jr, L. Suárez, M. Neuer, Z. Ibrahimović) international reputation is 5. It means, they know to more people in world wide.

Weak foot:

data.weak_foot.value_counts()

3    11347
2     3865
4     2666
5      245
1      155
Name: weak_foot, dtype: int64

sns.countplot(data.weak_foot)
plt.show()
print('Insights:')
print('more players weak foot at 3')

Insights:
more players weak foot at 3

Top 10 Left Footed Football Players with Weak_foot

data[data['preferred_foot'] == 'Left'][['short_name','weak_foot', 'club', 'nationality']].head(10).style.background_gradient('magma')

	short_name	weak_foot	club	nationality
0	L. Messi	4	FC Barcelona	Argentina
9	M. Salah	3	Liverpool	Egypt
16	G. Chiellini	3	Juventus	Italy
22	A. Griezmann	3	FC Barcelona	France
23	P. Dybala	3	Juventus	Argentina
25	Ederson	3	Manchester City	Brazil
28	T. Courtois	2	Real Madrid	Belgium
32	H. Lloris	1	Tottenham Hotspur	France
33	David Silva	2	Manchester City	Spain
40	A. Laporte	3	Manchester City	France

Top 10 Right Footed Football Players with Weak_foot

data[data['preferred_foot'] == 'Right'][['short_name','weak_foot',  'club', 'nationality']].head(10).style.background_gradient('viridis')

	short_name	weak_foot	club	nationality
1	Cristiano Ronaldo	4	Juventus	Portugal
2	Neymar Jr	5	Paris Saint-Germain	Brazil
3	J. Oblak	3	Atlético Madrid	Slovenia
4	E. Hazard	4	Real Madrid	Belgium
5	K. De Bruyne	5	Manchester City	Belgium
6	M. ter Stegen	4	FC Barcelona	Germany
7	V. van Dijk	3	Liverpool	Netherlands
8	L. Modrić	4	Real Madrid	Croatia
10	K. Mbappé	4	Paris Saint-Germain	France
11	K. Koulibaly	3	Napoli	Senegal

Skill_moves:

sns.countplot(data.skill_moves)
plt.show()
print('Insights:')
print('more players skill_means at 2')

Insights:
more players skill_means at 2

Work rate

data['work_rate'].value_counts()

Medium/Medium    9875
High/Medium      3216
Medium/High      1718
High/High         986
Medium/Low        886
High/Low          684
Low/Medium        461
Low/High          417
Low/Low            35
Name: work_rate, dtype: int64

plt.figure(figsize=(14, 7))
sns.countplot(data.work_rate)
plt.show()
print('Insights:')
print(len(data.loc[data['work_rate'] == 'Medium/High']),'players have Medium/Medium work rates followed by High/Medium work rates')

Insights:
1718 players have Medium/Medium work rates followed by High/Medium work rates

Let's see the players who have 'High/High' Work Rate

len(data.loc[data['work_rate'] == 'High/High'])

986

986 players work rate is high/high

Let's see the players who have 'Low/Low' Work Rate

len((data.loc[data['work_rate'] == 'Low/Low']))

35

35 players work rate is low/low

BMI:-(Body Mass Index)

Let us now create a new feature BMI from weight and height, and analyse its effect on performance ratings.

data['bmi'] = data['weight_kg'] // (data['height_cm']/100)**2

plt.figure(figsize= (14, 7))

ax = sns.countplot(x='bmi', data=data, order=data.bmi.value_counts().iloc[:20].index)
ax.set_title(label='Count of Players on Basis of BMI(Body Mass Index) in FIFA 20')

ax.set_xlabel(xlabel='BMI(Body Mass Index)')
ax.set_ylabel(ylabel='Count')

plt.show()

we can see that, 22 is the most common BMI

fig = go.Figure()

fig = go.Figure(data=go.Scatter(
    x = data['bmi'],
    y = data['overall'],
    mode='markers',
    marker=dict(
        color=data['overall'], 
        showscale=True
    ),
    text= data['short_name'],
))

fig.update_layout(title='BMI vs Overall Rating',
                  xaxis_title='BMI',
                  yaxis_title='Overall Rating')
fig.show()

We observe that:

Highest rating for BMI 24.

No direct relation.

Maximum of rating first increases and then decreases.

Data preprocessing

Copying the data in to different variable:

data_copy=data.copy()
data_copy.head(1)

	sofifa_id	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb	bmi
0	158023	https://sofifa.com/player/158023/lionel-messi/...	L. Messi	Lionel Andrés Messi Cuccittini	32	1987-06-24	170	72	Argentina	FC Barcelona	94	94	95500000	565000	RW, CF, ST	Left	5	4	4	Medium/Low	Messi	Yes	195800000.0	#Dribbler, #Distance Shooter, #Crosser, #FK Sp...	RW	10.0	NaN	2004-07-01	2021.0	NaN	NaN	87.0	92.0	92.0	96.0	39.0	66.0	NaN	NaN	NaN	NaN	NaN	NaN	Beat Offside Trap, Argues with Officials, Earl...	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89+2	89+2	89+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	93+2	92+2	87+2	87+2	87+2	92+2	68+2	66+2	66+2	66+2	68+2	63+2	52+2	52+2	52+2	63+2	24.0

columns1 = ['dribbling','defending','physic','passing','shooting','pace','ls','st','rs','lw','lf','cf','rf','rw','lam',
            'cam','ram','lm','lcm','cm','rcm','rm','lwb','ldm','cdm','rdm','rwb','lb','lcb','cb','rcb','rb']

Last 26 columns Cleanning, Process and Assign the new attributes to columns listed below.

columns = ['ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram',
       'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb',
       'lcb', 'cb', 'rcb', 'rb']

Let's omit the '+' sign

for col in columns:
  data_copy[col]=data_copy[col].str.split('+',n=1,expand = True)[0]

data_copy[columns]

	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	89	89	89	93	93	93	93	93	93	93	93	92	87	87	87	92	68	66	66	66	68	63	52	52	52	63
1	91	91	91	89	90	90	90	89	88	88	88	88	81	81	81	88	65	61	61	61	65	61	53	53	53	61
2	84	84	84	90	89	89	89	90	90	90	90	89	82	82	82	89	66	61	61	61	66	61	46	46	46	61
3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	83	83	83	89	88	88	88	89	89	89	89	89	83	83	83	89	66	63	63	63	66	61	49	49	49	61
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18273	32	32	32	31	31	31	31	31	31	31	31	33	33	33	33	33	43	42	42	42	43	45	46	46	46	45
18274	33	33	33	33	32	32	32	33	33	33	33	35	36	36	36	35	44	43	43	43	44	46	47	47	47	46
18275	43	43	43	43	43	43	43	43	44	44	44	45	46	46	46	45	47	49	49	49	47	47	49	49	49	47
18276	43	43	43	45	44	44	44	45	46	46	46	46	46	46	46	46	48	48	48	48	48	48	49	49	49	48
18277	42	42	42	44	43	43	43	44	46	46	46	46	46	46	46	46	48	49	49	49	48	48	50	50	50	48

18278 rows × 26 columns

Let's Replace NaN values with 0 and converting it into int

data_copy[columns] = data_copy[columns].fillna(0)
data_copy[columns] = data_copy[columns].astype(int)

Finding Median to the columns:

median = data_copy[columns].median(skipna=True)
median

ls     57.0
st     57.0
rs     57.0
lw     59.0
lf     58.0
cf     58.0
rf     58.0
rw     59.0
lam    58.0
cam    58.0
ram    58.0
lm     59.0
lcm    57.0
cm     57.0
rcm    57.0
rm     59.0
lwb    56.0
ldm    56.0
cdm    56.0
rdm    56.0
rwb    56.0
lb     56.0
lcb    55.0
cb     55.0
rcb    55.0
rb     56.0
dtype: float64

Median imputation to the columns

data_copy[columns]=data_copy[columns].replace(0,data_copy[columns].median())
data_copy[columns]

	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	89	89	89	93	93	93	93	93	93	93	93	92	87	87	87	92	68	66	66	66	68	63	52	52	52	63
1	91	91	91	89	90	90	90	89	88	88	88	88	81	81	81	88	65	61	61	61	65	61	53	53	53	61
2	84	84	84	90	89	89	89	90	90	90	90	89	82	82	82	89	66	61	61	61	66	61	46	46	46	61
3	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
4	83	83	83	89	88	88	88	89	89	89	89	89	83	83	83	89	66	63	63	63	66	61	49	49	49	61
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18273	32	32	32	31	31	31	31	31	31	31	31	33	33	33	33	33	43	42	42	42	43	45	46	46	46	45
18274	33	33	33	33	32	32	32	33	33	33	33	35	36	36	36	35	44	43	43	43	44	46	47	47	47	46
18275	43	43	43	43	43	43	43	43	44	44	44	45	46	46	46	45	47	49	49	49	47	47	49	49	49	47
18276	43	43	43	45	44	44	44	45	46	46	46	46	46	46	46	46	48	48	48	48	48	48	49	49	49	48
18277	42	42	42	44	43	43	43	44	46	46	46	46	46	46	46	46	48	49	49	49	48	48	50	50	50	48

18278 rows × 26 columns

data_copy[columns].dtypes

ls     int32
st     int32
rs     int32
lw     int32
lf     int32
cf     int32
rf     int32
rw     int32
lam    int32
cam    int32
ram    int32
lm     int32
lcm    int32
cm     int32
rcm    int32
rm     int32
lwb    int32
ldm    int32
cdm    int32
rdm    int32
rwb    int32
lb     int32
lcb    int32
cb     int32
rcb    int32
rb     int32
dtype: object

Removing the misssing values in these 6 columns and median imputation

columns1 = ['dribbling','defending','physic','passing','shooting','pace']

for col in columns1:
  data_copy[col] = data_copy[col].fillna(data_copy[col].median())
data_copy[columns1]

	dribbling	defending	physic	passing	shooting	pace
0	96.0	39.0	66.0	92.0	92.0	87.0
1	89.0	35.0	78.0	82.0	93.0	90.0
2	95.0	32.0	58.0	87.0	85.0	91.0
3	64.0	56.0	66.0	58.0	54.0	69.0
4	94.0	35.0	66.0	86.0	83.0	91.0
...	...	...	...	...	...	...
18273	33.0	47.0	51.0	28.0	23.0	57.0
18274	35.0	48.0	48.0	33.0	24.0	58.0
18275	45.0	48.0	51.0	44.0	35.0	54.0
18276	47.0	45.0	52.0	47.0	35.0	59.0
18277	45.0	47.0	55.0	51.0	32.0	60.0

18278 rows × 6 columns

data_copy[columns1]=data_copy[columns1].astype(int)
data_copy[columns1].dtypes
data_copy[columns1].dtypes

dribbling    int32
defending    int32
physic       int32
passing      int32
shooting     int32
pace         int32
dtype: object

Removing missing values in goal keeper related columns and replacing nan's into the zero(0)

columns3=['gk_diving','gk_handling','gk_kicking','gk_reflexes','gk_speed','gk_positioning']

data_copy[columns3] = data_copy[columns3].fillna(0)
data_copy[columns3]=data_copy[columns3].astype(int)
data_copy[columns3]

	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning
0	0	0	0	0	0	0
1	0	0	0	0	0	0
2	0	0	0	0	0	0
3	87	92	78	89	52	90
4	0	0	0	0	0	0
...	...	...	...	...	...	...
18273	0	0	0	0	0	0
18274	0	0	0	0	0	0
18275	0	0	0	0	0	0
18276	0	0	0	0	0	0
18277	0	0	0	0	0	0

18278 rows × 6 columns

Replacing the nan values with mode in the team_position column:

#missing values inthe team_position column
data_copy.team_position.isnull().sum()

240

Before imputation

data_copy['team_position'].value_counts()

SUB    7820
RES    2958
GK      662
LCB     660
RCB     660
LB      560
RB      560
ST      458
LCM     411
RCM     411
RM      399
LM      398
CAM     311
RDM     244
LDM     242
LS      195
RS      195
CDM     181
LW      162
RW      161
CB      100
CM       76
LWB      58
RWB      58
LAM      23
RAM      23
LF       19
RF       19
CF       14
Name: team_position, dtype: int64

Mode imputation:

data_copy['team_position'].fillna(data_copy['team_position'].mode()[0], inplace = True)

After mode imputation:

data_copy['team_position'].value_counts()

SUB    8060
RES    2958
GK      662
LCB     660
RCB     660
LB      560
RB      560
ST      458
LCM     411
RCM     411
RM      399
LM      398
CAM     311
RDM     244
LDM     242
LS      195
RS      195
CDM     181
LW      162
RW      161
CB      100
CM       76
LWB      58
RWB      58
LAM      23
RAM      23
LF       19
RF       19
CF       14
Name: team_position, dtype: int64

Dropping irrelevant:

data_copy.columns

Index(['sofifa_id', 'player_url', 'short_name', 'long_name', 'age', 'dob',
       'height_cm', 'weight_kg', 'nationality', 'club',
       ...
       'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb', 'bmi'],
      dtype='object', length=105)

Droping these columns:

droping_column=['long_name','player_url','body_type','real_face','sofifa_id','dob','player_tags',
                'loaned_from','joined','nation_position','player_traits','release_clause_eur','team_jersey_number','contract_valid_until','nation_jersey_number']

data_copy = data_copy.drop(droping_column, axis = 1)

data_copy.head(2)

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	team_position	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb	bmi
0	L. Messi	32	170	72	Argentina	FC Barcelona	94	94	95500000	565000	RW, CF, ST	Left	5	4	4	Medium/Low	RW	87	92	92	96	39	66	0	0	0	0	0	0	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89	89	89	93	93	93	93	93	93	93	93	92	87	87	87	92	68	66	66	66	68	63	52	52	52	63	24.0
1	Cristiano Ronaldo	34	187	83	Portugal	Juventus	93	93	58500000	405000	ST, LW	Right	5	4	5	High/Low	LW	90	93	82	89	35	78	0	0	0	0	0	0	84	94	89	83	87	89	81	76	77	92	89	91	87	96	71	95	95	85	78	93	63	29	95	82	85	95	28	32	24	7	11	15	14	11	91	91	91	89	90	90	90	89	88	88	88	88	81	81	81	88	65	61	61	61	65	61	53	53	53	61	23.0

data_copy.columns

Index(['short_name', 'age', 'height_cm', 'weight_kg', 'nationality', 'club',
       'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_diving',
       'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',
       'gk_positioning', 'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'mentality_composure', 'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb', 'bmi'],
      dtype='object')

data_copy.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18278 entries, 0 to 18277
Data columns (total 90 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   short_name                  18278 non-null  object 
 1   age                         18278 non-null  int64  
 2   height_cm                   18278 non-null  int64  
 3   weight_kg                   18278 non-null  int64  
 4   nationality                 18278 non-null  object 
 5   club                        18278 non-null  object 
 6   overall                     18278 non-null  int64  
 7   potential                   18278 non-null  int64  
 8   value_eur                   18278 non-null  int64  
 9   wage_eur                    18278 non-null  int64  
 10  player_positions            18278 non-null  object 
 11  preferred_foot              18278 non-null  object 
 12  international_reputation    18278 non-null  int64  
 13  weak_foot                   18278 non-null  int64  
 14  skill_moves                 18278 non-null  int64  
 15  work_rate                   18278 non-null  object 
 16  team_position               18278 non-null  object 
 17  pace                        18278 non-null  int32  
 18  shooting                    18278 non-null  int32  
 19  passing                     18278 non-null  int32  
 20  dribbling                   18278 non-null  int32  
 21  defending                   18278 non-null  int32  
 22  physic                      18278 non-null  int32  
 23  gk_diving                   18278 non-null  int32  
 24  gk_handling                 18278 non-null  int32  
 25  gk_kicking                  18278 non-null  int32  
 26  gk_reflexes                 18278 non-null  int32  
 27  gk_speed                    18278 non-null  int32  
 28  gk_positioning              18278 non-null  int32  
 29  attacking_crossing          18278 non-null  int64  
 30  attacking_finishing         18278 non-null  int64  
 31  attacking_heading_accuracy  18278 non-null  int64  
 32  attacking_short_passing     18278 non-null  int64  
 33  attacking_volleys           18278 non-null  int64  
 34  skill_dribbling             18278 non-null  int64  
 35  skill_curve                 18278 non-null  int64  
 36  skill_fk_accuracy           18278 non-null  int64  
 37  skill_long_passing          18278 non-null  int64  
 38  skill_ball_control          18278 non-null  int64  
 39  movement_acceleration       18278 non-null  int64  
 40  movement_sprint_speed       18278 non-null  int64  
 41  movement_agility            18278 non-null  int64  
 42  movement_reactions          18278 non-null  int64  
 43  movement_balance            18278 non-null  int64  
 44  power_shot_power            18278 non-null  int64  
 45  power_jumping               18278 non-null  int64  
 46  power_stamina               18278 non-null  int64  
 47  power_strength              18278 non-null  int64  
 48  power_long_shots            18278 non-null  int64  
 49  mentality_aggression        18278 non-null  int64  
 50  mentality_interceptions     18278 non-null  int64  
 51  mentality_positioning       18278 non-null  int64  
 52  mentality_vision            18278 non-null  int64  
 53  mentality_penalties         18278 non-null  int64  
 54  mentality_composure         18278 non-null  int64  
 55  defending_marking           18278 non-null  int64  
 56  defending_standing_tackle   18278 non-null  int64  
 57  defending_sliding_tackle    18278 non-null  int64  
 58  goalkeeping_diving          18278 non-null  int64  
 59  goalkeeping_handling        18278 non-null  int64  
 60  goalkeeping_kicking         18278 non-null  int64  
 61  goalkeeping_positioning     18278 non-null  int64  
 62  goalkeeping_reflexes        18278 non-null  int64  
 63  ls                          18278 non-null  int32  
 64  st                          18278 non-null  int32  
 65  rs                          18278 non-null  int32  
 66  lw                          18278 non-null  int32  
 67  lf                          18278 non-null  int32  
 68  cf                          18278 non-null  int32  
 69  rf                          18278 non-null  int32  
 70  rw                          18278 non-null  int32  
 71  lam                         18278 non-null  int32  
 72  cam                         18278 non-null  int32  
 73  ram                         18278 non-null  int32  
 74  lm                          18278 non-null  int32  
 75  lcm                         18278 non-null  int32  
 76  cm                          18278 non-null  int32  
 77  rcm                         18278 non-null  int32  
 78  rm                          18278 non-null  int32  
 79  lwb                         18278 non-null  int32  
 80  ldm                         18278 non-null  int32  
 81  cdm                         18278 non-null  int32  
 82  rdm                         18278 non-null  int32  
 83  rwb                         18278 non-null  int32  
 84  lb                          18278 non-null  int32  
 85  lcb                         18278 non-null  int32  
 86  cb                          18278 non-null  int32  
 87  rcb                         18278 non-null  int32  
 88  rb                          18278 non-null  int32  
 89  bmi                         18278 non-null  float64
dtypes: float64(1), int32(38), int64(44), object(7)
memory usage: 9.9+ MB

column_r=['short_name','age','team_position','overall','potential','pace','shooting','passing','dribbling','defending','physic']

Soccer Positions

• Attacker
• Defender
• Midfielder
• Goalkeeper

Attacker

attack=['LW','LS','LF','RS','RW','RF','ST','CF']
attack_data= data.query('team_position in @attack')
fig= px.pie(attack_data, names='team_position', color_discrete_sequence= px.colors.sequential.Inferno,
           title='Percentage of Player Attacking Positions')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()

Attacking Positions:-

ST:- Striker LS:- Left Striker RS:- Right Striker LW:- Left Winger RW:- Right Winger RF:- Right Forward LF:- Left Forward CF:- Center Forward

attack = ['LW', 'RW', 'ST', 'LF', 'RF', 'CF', 'LS', 'RS']
attack_data = data_copy.query('team_position in @attack')
attack_data=attack_data[column_r]
attack_data=attack_data.set_index(['short_name','team_position','age'])
attack_data.head(2)

			overall	potential	pace	shooting	passing	dribbling	defending	physic
short_name	team_position	age
L. Messi	RW	32	94	94	87	92	92	96	39	66
Cristiano Ronaldo	LW	34	93	93	90	93	82	89	35	78

Defender

defence = ['LWB', 'RWB', 'CB', 'LB', 'RB', 'LCB', 'RCB']
defence_data = data.query('team_position in @defence')    
fig = px.pie(defence_data, names='team_position',
             color_discrete_sequence=px.colors.sequential.Inferno ,
             title='Percentages of Player Defending Positions')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()

Defending Positions:-

LCB:- Left Center Back RCB:- Right Center Back LB:- Left Back RB:- Right Back
CB:- Center Back RWB:- Right Wing Back LWB:- Left Wing Back

defence = ['LWB', 'RWB', 'CB', 'LB', 'RB', 'LCB', 'RCB']
defence_data = data_copy.query('team_position in @defence')
defence_data =defence_data[column_r]
defence_data =defence_data.set_index(['short_name','team_position','age'])
defence_data.head(2)

			overall	potential	pace	shooting	passing	dribbling	defending	physic
short_name	team_position	age
V. van Dijk	LCB	27	90	91	77	60	70	71	90	86
K. Koulibaly	LCB	28	89	91	71	28	54	67	89	87

Midfielder

mid = ['CM', 'RCM', 'LCM', 'RM', 'LM', 'CAM', 'RDM', 'LDM', 'CDM', 'RAM', 'LAM']
mid_data = data.query('team_position in @mid')    
fig = px.pie(mid_data, names='team_position',
             color_discrete_sequence=px.colors.sequential.Inferno ,
             title='Percentages of Player Midfield Positions')
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()

Midfield Positions:-

RCM:- Right Centra Midfielder LCM:- Left Centra Midfielder RM:- Right Midfielder LM:- Left Midfielder CAM:- Central Attacking Midfielder RDM:- Right Defensive Midfielder LDM:- Left Defensive Midfielder CDM:- Central Defensive Midfielder CM:- Central Midfielder RAM:- Right Attacking Midfielder LAM:- Left Attacking Midfielder

Midfield = ['CM', 'RCM', 'LCM', 'RM', 'LM', 'CAM', 'RDM', 'LDM', 'CDM', 'RAM', 'LAM']
mid_data = data_copy.query('team_position in @Midfield') 
mid_data =mid_data[column_r]
mid_data =mid_data.set_index(['short_name','age','team_position'])
mid_data.head(2)

			overall	potential	pace	shooting	passing	dribbling	defending	physic
short_name	age	team_position
Neymar Jr	27	CAM	92	92	91	85	87	95	32	58
K. De Bruyne	28	RCM	91	91	76	86	92	86	61	78

Goal_Keepers

 Goal_column=['short_name','age','team_position','goalkeeping_diving','goalkeeping_handling','goalkeeping_kicking','goalkeeping_positioning','goalkeeping_reflexes']
 

Goal_keep=['GK']
Goal_keep_data = data_copy.query('team_position in @Goal_keep') 
Goal_keep_data=Goal_keep_data[Goal_column]
Goal_keep_data=Goal_keep_data.set_index(['short_name','age','team_position'])
Goal_keep_data.head(2)

			goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes
short_name	age	team_position
J. Oblak	26	GK	87	92	78	90	89
M. ter Stegen	27	GK	88	85	88	88	90

Correlation Heatmap

We have made a seperate dataframe for the numeric columns from data to analyse.

numeric_features=numeric_features = ['age','height_cm','weight_kg','overall','potential','value_eur',
                 'wage_eur','international_reputation','weak_foot',
                 'skill_moves','release_clause_eur']
numeric_dataset1 = data[numeric_features]

plt.figure(figsize=(15,10))
sns.heatmap(numeric_dataset1.corr(), annot=True, cbar=True)

<AxesSubplot:>

We observe that there is high correlation between the following features:-

wage and value

release clause and value

release clause and age

In such cases, We should remove one of the features of these pairs when using the data for training to get better results.

Top players on the basis of their positions with the represenation polar chart:

top_players =  data_copy[data_copy.short_name.isin(['L. Messi', 'V. van Dijk', 'Neymar Jr'])]
top_players = top_players[['short_name','team_position','overall','potential','pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic']]
top_players

	short_name	team_position	overall	potential	pace	shooting	passing	dribbling	defending	physic
0	L. Messi	RW	94	94	87	92	92	96	39	66
2	Neymar Jr	CAM	92	92	91	85	87	95	32	58
7	V. van Dijk	LCB	90	91	77	60	70	71	90	86

# Library for building interactive charts
import plotly.graph_objects as go
# Module for creating blocks
from plotly.subplots import make_subplots

def create_polarcharts(
    stats: list,
    color: str,
    img_link: str,
    name_one: str,
    name_two: str
):
    '''
    The function accepts the following arguments:
    
        stats - takes a list of numeric values of characteristics
        color - takes the color of the lines in the diagram
        img_link - accepts a link to an image of a football player
        name_one - takes the name of the footballer
        name_two - accepts any additional text
    
    '''
    # Determine the number of rows and columns
    fig = make_subplots(rows=1, cols=2, 
                        # We indicate the types of graphs in each block
                        specs=[[{'type': 'xy'}, {"type": "polar"}]], 
                        # Setting the width of each column
                        column_widths=[0.5, 0.5])  
    
    # Create a Polar Chart
    fig.add_trace(go.Scatterpolar(
                     # Passing numeric parameters
                     r=stats,
                     # Passing parameter names
                     theta=['Pace', 'Shooting', 'Passing', 'Dribbling', 'Defending', 'Physic'],
                     # Setting the fill parameter
                     fill='toself',
                     # Specify the signature on hover
                     hovertemplate='<b>%{theta}</b>' + f'<b>: '+'%{r}',
                     # Specify a caption for the legend
                     name='',
                     # Specifying the line color
                     line=dict(color=color)),
                     # Specify line and column numbers
                     row=1, col=2)
    
    # Add an image to the chart
    fig.layout.images = [dict(
               # Passing a link to the image
               source=img_link,
               # Specify the position of the image along the x-axis
               x=0.2, 
               # Specify the position of the image along the y-axis
               y=0.5,
               # Setting the size of the chart
               sizex=1, 
               sizey=1.6,
               # Setting the position along the x-axis
               xanchor="center", 
               # Setting the y-axis position
               yanchor="middle",
               # Place the image under the chart
               layer="below"
                             )
                        ]

    fig.update_layout(
        # Set the name of the chart
        title=f'<b>{name_one}</b><br><sub>{name_two}</sub>',
        # Setting the background color
        paper_bgcolor="rgb(223, 223, 223)",
        # Setting the chart theme
        template='xgridoff',
        # Passing chart parameters
        polar=dict(
             # Background color
             bgcolor="rgb(223, 223, 223)",
             # Adding a line with numeric divisions
             radialaxis=dict(
                        # Displaying the line
                        visible = True,
                        # Set the range of divisions
                        range = [0, 100]
                            )
                   ), 
        # Passing the parameters to the font
        font=dict(
                  # Font type
                  family='Arials',
                  # Font size
                  size=15,
                  # Font color
                  color='Black'
                 )
    )

    # Displaying the graph
    fig.show()

create_polarcharts([87, 92, 92, 96, 39, 66], 
                   'MidnightBlue',
                   'https://th.bing.com/th/id/OIP.2f8gz-gDSPVyFhpb16QpJQHaLw?w=115&h=183&c=7&o=5&dpr=1.25&pid=1.7', 'L. Messi', 'Attacker')

create_polarcharts([91, 85, 87, 95, 32, 58], 
                   'MidnightBlue',
                   'data:image/jpeg;base64,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', 
                   'Neymar Jr', 
                   'Midfielder')

#J. Oblak
create_polarcharts([77, 60, 70, 71,90, 86], 
                   'MidnightBlue',
                   'https://t.resfu.com/img_data/players/big/107732.jpg', 
                   'V. van Dijk', 
                   'Defender')

def create_polarcharts(
    stats: list,
    color: str,
    img_link: str,
    name_one: str,
    name_two: str
):
    '''
    The function accepts the following arguments:
    
        stats - takes a list of numeric values of characteristics
        color - takes the color of the lines in the diagram
        img_link - accepts a link to an image of a football player
        name_one - takes the name of the footballer
        name_two - accepts any additional text
    
    '''
    # Determine the number of rows and columns
    fig = make_subplots(rows=1, cols=2, 
                        # We indicate the types of graphs in each block
                        specs=[[{'type': 'xy'}, {"type": "polar"}]], 
                        # Setting the width of each column
                        column_widths=[0.5, 0.5])  
    
    # Create a Polar Chart
    fig.add_trace(go.Scatterpolar(
                     # Passing numeric parameters
                     r=stats,
                     # Passing parameter names
                     theta=['goalkeeping_diving','goalkeeping_handling','goalkeeping_kicking','goalkeeping_positioning','goalkeeping_reflexes'],
                     # Setting the fill parameter
                     fill='toself',
                     # Specify the signature on hover
                     hovertemplate='<b>%{theta}</b>' + f'<b>: '+'%{r}',
                     # Specify a caption for the legend
                     name='',
                     # Specifying the line color
                     line=dict(color=color)),
                     # Specify line and column numbers
                     row=1, col=2)
    
    # Add an image to the chart
    fig.layout.images = [dict(
               # Passing a link to the image
               source=img_link,
               # Specify the position of the image along the x-axis
               x=0.2, 
               # Specify the position of the image along the y-axis
               y=0.5,
               # Setting the size of the chart
               sizex=1, 
               sizey=1.6,
               # Setting the position along the x-axis
               xanchor="center", 
               # Setting the y-axis position
               yanchor="middle",
               # Place the image under the chart
               layer="below"
                             )
                        ]

    fig.update_layout(
        # Set the name of the chart
        title=f'<b>{name_one}</b><br><sub>{name_two}</sub>',
        # Setting the background color
        paper_bgcolor="rgb(223, 223, 223)",
        # Setting the chart theme
        template='xgridoff',
        # Passing chart parameters
        polar=dict(
             # Background color
             bgcolor="rgb(223, 223, 223)",
             # Adding a line with numeric divisions
             radialaxis=dict(
                        # Displaying the line
                        visible = True,
                        # Set the range of divisions
                        range = [0, 100]
                            )
                   ), 
        # Passing the parameters to the font
        font=dict(
                  # Font type
                  family='Arials',
                  # Font size
                  size=14,
                  # Font color
                  color='Black'
                 )
    )

    # Displaying the graph
    fig.show()

create_polarcharts([87, 92, 92, 96, 39], 
                   'MidnightBlue',
                   'https://sky.shorthandstories.com/diego-simeone-atletico/assets/qcniKrZYqh/gettyimages-1282561174-1496x2042.jpeg', 'J. Oblak', 'Goalkeepers')

data_final=data_copy[['short_name','age', 'height_cm', 'weight_kg', 'nationality', 'club',
       'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_diving',
       'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',
       'gk_positioning', 'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'mentality_composure', 'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']]

data_final

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	team_position	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	L. Messi	32	170	72	Argentina	FC Barcelona	94	94	95500000	565000	RW, CF, ST	Left	5	4	4	Medium/Low	RW	87	92	92	96	39	66	0	0	0	0	0	0	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89	89	89	93	93	93	93	93	93	93	93	92	87	87	87	92	68	66	66	66	68	63	52	52	52	63
1	Cristiano Ronaldo	34	187	83	Portugal	Juventus	93	93	58500000	405000	ST, LW	Right	5	4	5	High/Low	LW	90	93	82	89	35	78	0	0	0	0	0	0	84	94	89	83	87	89	81	76	77	92	89	91	87	96	71	95	95	85	78	93	63	29	95	82	85	95	28	32	24	7	11	15	14	11	91	91	91	89	90	90	90	89	88	88	88	88	81	81	81	88	65	61	61	61	65	61	53	53	53	61
2	Neymar Jr	27	175	68	Brazil	Paris Saint-Germain	92	92	105500000	290000	LW, CAM	Right	5	5	5	High/Medium	CAM	91	85	87	95	32	58	0	0	0	0	0	0	87	87	62	87	87	96	88	87	81	95	94	89	96	92	84	80	61	81	49	84	51	36	87	90	90	94	27	26	29	9	9	15	15	11	84	84	84	90	89	89	89	90	90	90	90	89	82	82	82	89	66	61	61	61	66	61	46	46	46	61
3	J. Oblak	26	188	87	Slovenia	Atlético Madrid	91	93	77500000	125000	GK	Right	3	3	1	Medium/Medium	GK	69	54	58	64	56	66	87	92	78	89	52	90	13	11	15	43	13	12	13	14	40	30	43	60	67	88	49	59	78	41	78	12	34	19	11	65	11	68	27	12	18	87	92	78	90	89	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
4	E. Hazard	28	175	74	Belgium	Real Madrid	91	91	90000000	470000	LW, CF	Right	4	4	4	High/Medium	LW	91	83	86	94	35	66	0	0	0	0	0	0	81	84	61	89	83	95	83	79	83	94	94	88	95	90	94	82	56	84	63	80	54	41	87	89	88	91	34	27	22	11	12	6	8	8	83	83	83	89	88	88	88	89	89	89	89	89	83	83	83	89	66	63	63	63	66	61	49	49	49	61
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18273	Shao Shuai	22	186	79	China PR	Beijing Renhe FC	48	56	40000	2000	CB	Right	1	3	2	Medium/High	RES	57	23	28	33	47	51	0	0	0	0	0	0	21	17	40	35	27	24	23	21	28	35	56	58	45	40	73	36	70	60	47	16	46	48	28	25	39	41	45	50	52	5	5	13	5	14	32	32	32	31	31	31	31	31	31	31	31	33	33	33	33	33	43	42	42	42	43	45	46	46	46	45
18274	Xiao Mingjie	22	177	66	China PR	Shanghai SIPG FC	48	56	40000	2000	CB	Right	1	2	2	Medium/Medium	SUB	58	24	33	35	48	48	0	0	0	0	0	0	24	20	42	43	28	32	24	29	39	31	55	61	43	41	76	33	72	55	44	20	42	49	23	25	37	35	42	53	57	13	6	14	11	9	33	33	33	33	32	32	32	33	33	33	33	35	36	36	36	35	44	43	43	43	44	46	47	47	47	46
18275	Zhang Wei	19	186	75	China PR	Hebei China Fortune FC	48	56	40000	1000	CM	Right	1	2	2	Low/Medium	SUB	54	35	44	45	48	51	0	0	0	0	0	0	32	33	49	53	32	40	32	32	55	49	55	54	52	52	57	48	60	50	51	26	50	45	38	38	36	39	46	52	46	7	8	10	6	14	43	43	43	43	43	43	43	43	44	44	44	45	46	46	46	45	47	49	49	49	47	47	49	49	49	47
18276	Wang Haijian	18	185	74	China PR	Shanghai Greenland Shenhua FC	48	54	40000	1000	CM	Right	1	2	2	Low/Low	SUB	59	35	47	47	45	52	0	0	0	0	0	0	39	34	47	54	28	42	37	39	48	49	55	63	55	54	59	46	61	42	55	28	57	49	31	48	36	40	39	44	54	14	9	13	13	13	43	43	43	45	44	44	44	45	46	46	46	46	46	46	46	46	48	48	48	48	48	48	49	49	49	48
18277	Pan Ximing	26	182	78	China PR	Hebei China Fortune FC	48	51	40000	2000	CM	Right	1	3	2	Medium/Medium	SUB	60	32	51	45	47	55	0	0	0	0	0	0	44	30	48	58	25	40	34	35	52	46	54	64	52	49	71	41	64	40	61	30	54	47	29	52	38	42	52	42	48	8	12	14	9	10	42	42	42	44	43	43	43	44	46	46	46	46	46	46	46	46	48	49	49	49	48	48	50	50	50	48

18278 rows × 89 columns

data_final.columns

Index(['short_name', 'age', 'height_cm', 'weight_kg', 'nationality', 'club',
       'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_diving',
       'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',
       'gk_positioning', 'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'mentality_composure', 'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb'],
      dtype='object')

data_final.columns.isnull().sum()

0

data_final.describe(include='O')

	short_name	nationality	club	player_positions	preferred_foot	work_rate	team_position
count	18278	18278	18278	18278	18278	18278	18278
unique	17354	162	698	643	2	9	29
top	J. Rodríguez	England	Southampton	CB	Right	Medium/Medium	SUB
freq	11	1667	33	2322	13960	9875	8060

Label encoding

# Import label encoder
from sklearn import preprocessing
# label_encoder object knows how to understand word labels.
label_encoder = preprocessing.LabelEncoder()

data_final['nationality']= label_encoder.fit_transform(data_final['nationality'])
data_final['club']= label_encoder.fit_transform(data_final['club'])
data_final['player_positions']= label_encoder.fit_transform(data_final['player_positions'])
data_final['preferred_foot']= label_encoder.fit_transform(data_final['preferred_foot'])
data_final['work_rate']= label_encoder.fit_transform(data_final['work_rate'])

#data_final['team_position']= label_encoder.fit_transform(data_final['team_position'])

data_final

	short_name	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	team_position	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	L. Messi	32	170	72	5	224	94	94	95500000	565000	544	0	5	4	4	7	RW	87	92	92	96	39	66	0	0	0	0	0	0	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89	89	89	93	93	93	93	93	93	93	93	92	87	87	87	92	68	66	66	66	68	63	52	52	52	63
1	Cristiano Ronaldo	34	187	83	121	350	93	93	58500000	405000	620	1	5	4	5	1	LW	90	93	82	89	35	78	0	0	0	0	0	0	84	94	89	83	87	89	81	76	77	92	89	91	87	96	71	95	95	85	78	93	63	29	95	82	85	95	28	32	24	7	11	15	14	11	91	91	91	89	90	90	90	89	88	88	88	88	81	81	81	88	65	61	61	61	65	61	53	53	53	61
2	Neymar Jr	27	175	68	18	462	92	92	105500000	290000	344	1	5	5	5	2	CAM	91	85	87	95	32	58	0	0	0	0	0	0	87	87	62	87	87	96	88	87	81	95	94	89	96	92	84	80	61	81	49	84	51	36	87	90	90	94	27	26	29	9	9	15	15	11	84	84	84	90	89	89	89	90	90	90	90	89	82	82	82	89	66	61	61	61	66	61	46	46	46	61
3	J. Oblak	26	188	87	133	63	91	93	77500000	125000	226	1	3	3	1	8	GK	69	54	58	64	56	66	87	92	78	89	52	90	13	11	15	43	13	12	13	14	40	30	43	60	67	88	49	59	78	41	78	12	34	19	11	65	11	68	27	12	18	87	92	78	90	89	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
4	E. Hazard	28	175	74	12	505	91	91	90000000	470000	350	1	4	4	4	2	LW	91	83	86	94	35	66	0	0	0	0	0	0	81	84	61	89	83	95	83	79	83	94	94	88	95	90	94	82	56	84	63	80	54	41	87	89	88	91	34	27	22	11	12	6	8	8	83	83	83	89	88	88	88	89	89	89	89	89	83	83	83	89	66	63	63	63	66	61	49	49	49	61
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18273	Shao Shuai	22	186	79	28	79	48	56	40000	2000	55	1	1	3	2	6	RES	57	23	28	33	47	51	0	0	0	0	0	0	21	17	40	35	27	24	23	21	28	35	56	58	45	40	73	36	70	60	47	16	46	48	28	25	39	41	45	50	52	5	5	13	5	14	32	32	32	31	31	31	31	31	31	31	31	33	33	33	33	33	43	42	42	42	43	45	46	46	46	45
18274	Xiao Mingjie	22	177	66	28	574	48	56	40000	2000	55	1	1	2	2	8	SUB	58	24	33	35	48	48	0	0	0	0	0	0	24	20	42	43	28	32	24	29	39	31	55	61	43	41	76	33	72	55	44	20	42	49	23	25	37	35	42	53	57	13	6	14	11	9	33	33	33	33	32	32	32	33	33	33	33	35	36	36	36	35	44	43	43	43	44	46	47	47	47	46
18275	Zhang Wei	19	186	75	28	317	48	56	40000	1000	155	1	1	2	2	5	SUB	54	35	44	45	48	51	0	0	0	0	0	0	32	33	49	53	32	40	32	32	55	49	55	54	52	52	57	48	60	50	51	26	50	45	38	38	36	39	46	52	46	7	8	10	6	14	43	43	43	43	43	43	43	43	44	44	44	45	46	46	46	45	47	49	49	49	47	47	49	49	49	47
18276	Wang Haijian	18	185	74	28	573	48	54	40000	1000	155	1	1	2	2	4	SUB	59	35	47	47	45	52	0	0	0	0	0	0	39	34	47	54	28	42	37	39	48	49	55	63	55	54	59	46	61	42	55	28	57	49	31	48	36	40	39	44	54	14	9	13	13	13	43	43	43	45	44	44	44	45	46	46	46	46	46	46	46	46	48	48	48	48	48	48	49	49	49	48
18277	Pan Ximing	26	182	78	28	317	48	51	40000	2000	155	1	1	3	2	8	SUB	60	32	51	45	47	55	0	0	0	0	0	0	44	30	48	58	25	40	34	35	52	46	54	64	52	49	71	41	64	40	61	30	54	47	29	52	38	42	52	42	48	8	12	14	9	10	42	42	42	44	43	43	43	44	46	46	46	46	46	46	46	46	48	49	49	49	48	48	50	50	50	48

18278 rows × 89 columns

data_final = data_final[data_final.overall >= 87] # extracting players with overall  87
data_final
names = data_final.short_name.tolist()
team_positions=data_final.team_position.tolist()
data_final = data_final.drop(['short_name','team_position'], axis = 1)
data_final

	age	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
0	32	170	72	5	224	94	94	95500000	565000	544	0	5	4	4	7	87	92	92	96	39	66	0	0	0	0	0	0	88	95	70	92	88	97	93	94	92	96	91	84	93	95	95	86	68	75	68	94	48	40	94	94	75	96	33	37	26	6	11	15	14	8	89	89	89	93	93	93	93	93	93	93	93	92	87	87	87	92	68	66	66	66	68	63	52	52	52	63
1	34	187	83	121	350	93	93	58500000	405000	620	1	5	4	5	1	90	93	82	89	35	78	0	0	0	0	0	0	84	94	89	83	87	89	81	76	77	92	89	91	87	96	71	95	95	85	78	93	63	29	95	82	85	95	28	32	24	7	11	15	14	11	91	91	91	89	90	90	90	89	88	88	88	88	81	81	81	88	65	61	61	61	65	61	53	53	53	61
2	27	175	68	18	462	92	92	105500000	290000	344	1	5	5	5	2	91	85	87	95	32	58	0	0	0	0	0	0	87	87	62	87	87	96	88	87	81	95	94	89	96	92	84	80	61	81	49	84	51	36	87	90	90	94	27	26	29	9	9	15	15	11	84	84	84	90	89	89	89	90	90	90	90	89	82	82	82	89	66	61	61	61	66	61	46	46	46	61
3	26	188	87	133	63	91	93	77500000	125000	226	1	3	3	1	8	69	54	58	64	56	66	87	92	78	89	52	90	13	11	15	43	13	12	13	14	40	30	43	60	67	88	49	59	78	41	78	12	34	19	11	65	11	68	27	12	18	87	92	78	90	89	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
4	28	175	74	12	505	91	91	90000000	470000	350	1	4	4	4	2	91	83	86	94	35	66	0	0	0	0	0	0	81	84	61	89	83	95	83	79	83	94	94	88	95	90	94	82	56	84	63	80	54	41	87	89	88	91	34	27	22	11	12	6	8	8	83	83	83	89	88	88	88	89	89	89	89	89	83	83	83	89	66	63	63	63	66	61	49	49	49	61
5	28	181	70	12	400	91	91	90000000	370000	11	1	4	5	4	0	76	86	92	86	61	78	0	0	0	0	0	0	93	82	55	92	82	86	85	83	91	91	77	76	78	91	76	91	63	89	74	90	76	61	88	94	79	91	68	58	51	15	13	5	10	13	82	82	82	87	87	87	87	87	88	88	88	88	87	87	87	88	77	77	77	77	77	73	66	66	66	73
6	27	187	85	57	224	90	93	67500000	250000	226	1	3	4	1	8	69	54	58	64	56	66	88	85	88	90	45	88	18	14	11	61	14	21	18	12	63	30	38	50	37	86	43	66	79	35	78	10	43	22	11	70	25	70	25	13	10	88	85	88	88	90	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
7	27	193	92	108	389	90	91	78000000	200000	55	1	3	3	2	8	77	60	70	71	90	86	0	0	0	0	0	0	53	52	86	78	45	70	60	70	81	76	74	79	61	88	53	81	90	75	92	64	82	89	47	65	62	89	91	92	85	13	10	13	11	11	69	69	69	67	69	69	69	67	69	69	69	69	74	74	74	69	79	83	83	83	79	81	87	87	87	81
8	33	172	66	34	505	90	90	45000000	340000	155	1	4	4	4	0	74	76	89	89	72	66	0	0	0	0	0	0	86	72	55	92	76	87	85	78	88	92	77	71	92	89	93	79	68	85	58	82	62	82	79	91	82	92	68	76	71	13	9	7	14	9	77	77	77	84	83	83	83	84	86	86	86	85	87	87	87	85	81	81	81	81	81	79	72	72	72	79
9	27	175	71	43	389	90	90	80500000	240000	574	0	3	3	4	2	93	86	81	89	45	74	0	0	0	0	0	0	79	90	59	84	79	89	83	69	75	89	94	92	91	92	88	80	69	85	73	84	63	55	92	84	77	91	38	43	41	14	14	9	11	14	84	84	84	88	88	88	88	88	87	87	87	87	81	81	81	87	70	67	67	67	70	66	57	57	57	66
10	20	178	73	53	462	89	95	93500000	155000	634	1	3	4	5	1	96	84	78	90	39	75	0	0	0	0	0	0	78	89	77	82	79	91	79	63	70	90	96	96	92	89	83	83	76	84	76	79	62	38	89	80	70	84	34	34	32	13	5	7	11	6	86	86	86	87	87	87	87	87	86	86	86	86	78	78	78	86	66	62	62	62	66	63	54	54	54	63
11	28	187	89	129	425	89	91	67500000	150000	55	1	3	3	2	6	71	28	54	67	89	87	0	0	0	0	0	0	30	22	83	71	14	69	28	28	63	71	69	73	52	86	41	55	81	73	95	15	87	88	35	52	33	82	91	90	87	7	11	7	13	5	56	56	56	55	57	57	57	55	58	58	58	60	65	65	65	60	74	79	79	79	74	77	86	86	86	77
12	25	188	89	45	626	89	91	83000000	220000	595	1	3	4	3	0	70	91	79	81	47	83	0	0	0	0	0	0	75	94	86	81	85	80	78	68	83	84	68	72	71	91	73	90	79	87	84	86	78	35	93	81	90	91	56	36	38	8	10	11	14	11	86	86	86	82	84	84	84	82	83	83	83	82	79	79	79	82	65	66	66	66	65	62	60	60	60	62
13	26	191	91	18	389	89	91	58000000	155000	226	1	3	3	1	8	69	54	58	64	56	66	85	84	85	89	51	90	17	13	19	45	20	27	19	18	44	30	56	47	40	88	37	64	52	32	78	14	27	11	13	66	23	65	15	19	16	85	84	85	90	89	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
14	28	192	82	136	401	89	90	56000000	205000	226	1	4	3	1	8	69	54	58	64	56	66	90	84	81	92	58	85	17	13	21	50	13	18	21	19	47	38	57	58	63	87	43	61	67	43	60	12	38	30	12	65	29	68	25	21	13	90	84	81	85	92	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
15	28	168	72	53	141	89	90	66000000	235000	96	1	3	3	2	6	78	65	77	81	87	83	0	0	0	0	0	0	68	65	54	86	56	79	49	49	81	80	79	77	82	93	92	71	77	97	73	63	90	92	72	79	54	85	90	91	85	15	12	10	7	10	72	72	72	77	76	76	76	77	78	78	78	79	82	82	82	79	85	87	87	87	85	84	83	83	83	84
16	34	187	85	77	350	89	89	24500000	215000	55	0	4	3	2	6	68	46	58	60	90	82	0	0	0	0	0	0	54	33	83	65	45	59	60	31	65	61	61	73	57	82	57	78	89	59	89	49	91	88	28	50	50	84	94	91	89	3	3	2	4	3	58	58	58	55	56	56	56	55	56	56	56	57	62	62	62	57	73	77	77	77	73	76	86	86	86	76
17	31	173	70	5	400	89	89	60000000	300000	595	1	4	4	4	2	80	90	77	88	33	74	0	0	0	0	0	0	70	93	78	83	85	88	83	73	64	89	82	78	84	92	91	89	81	79	74	84	65	24	93	83	83	90	30	29	24	13	15	6	11	14	87	87	87	85	87	87	87	85	85	85	85	83	77	77	77	83	60	58	58	58	60	56	50	50	50	56
18	33	184	82	136	505	89	89	31500000	300000	55	1	4	3	3	2	72	68	75	73	87	85	0	0	0	0	0	0	66	63	92	80	69	65	74	72	83	83	74	71	78	87	66	79	93	80	85	62	90	88	67	71	86	84	85	87	90	11	8	9	7	11	74	74	74	72	73	73	73	72	74	74	74	73	78	78	78	73	80	84	84	84	80	82	87	87	87	82
19	32	182	86	155	224	89	89	53000000	355000	595	1	5	4	3	2	73	89	80	84	51	84	0	0	0	0	0	0	78	91	83	82	90	85	86	82	72	84	76	70	79	92	79	88	69	82	86	86	87	41	92	82	83	85	57	45	38	27	25	31	33	37	86	86	86	83	85	85	85	83	84	84	84	82	79	79	79	82	67	67	67	67	67	64	63	63	63	64
20	30	184	80	120	226	89	89	64500000	235000	595	1	4	4	4	2	77	87	74	85	41	82	0	0	0	0	0	0	62	88	85	82	88	85	77	85	65	88	77	77	78	90	82	87	84	78	84	84	81	39	91	77	86	86	34	42	19	15	6	12	8	10	86	86	86	82	85	85	85	82	83	83	83	80	77	77	77	80	61	62	62	62	61	58	57	57	57	58
21	30	189	76	136	224	89	89	55000000	300000	96	1	4	3	3	8	42	62	80	80	85	80	0	0	0	0	0	0	62	67	68	89	44	80	66	68	82	88	40	43	67	87	49	61	66	86	77	54	85	89	77	86	60	93	90	86	80	5	8	13	9	13	70	70	70	73	75	75	75	73	78	78	78	75	83	83	83	75	78	86	86	86	78	78	82	82	82	78
22	28	176	73	53	224	89	89	69000000	370000	153	0	4	3	4	0	81	86	84	89	57	72	0	0	0	0	0	0	83	89	84	85	87	88	86	85	82	90	82	81	90	92	83	82	89	87	63	83	73	49	90	86	86	89	59	54	49	14	8	14	13	14	85	85	85	87	87	87	87	87	87	87	87	86	83	83	83	86	73	71	71	71	73	70	65	65	65	70
23	25	177	75	5	350	88	92	76500000	215000	41	0	3	3	4	8	83	82	84	90	43	64	0	0	0	0	0	0	82	80	64	87	88	90	88	88	75	93	86	81	91	84	85	80	75	79	61	86	48	42	80	87	86	84	32	48	40	5	4	4	5	8	80	80	80	85	85	85	85	85	86	86	86	85	80	80	80	85	68	64	64	64	68	64	54	54	54	64
24	26	191	84	53	401	88	91	72500000	250000	170	1	4	4	5	2	74	81	86	85	66	86	0	0	0	0	0	0	80	75	75	86	84	87	85	82	90	90	67	79	75	82	66	90	82	87	89	82	78	64	83	88	83	87	63	67	65	5	6	2	4	3	81	81	81	82	83	83	83	82	84	84	84	83	84	84	84	83	76	77	77	77	76	74	72	72	72	74
25	25	188	86	18	400	88	91	54500000	185000	226	0	2	3	1	8	69	54	58	64	56	66	86	82	93	88	63	86	20	14	14	56	18	23	15	20	58	40	64	63	60	87	48	70	66	41	68	18	38	27	20	70	17	70	29	15	8	86	82	93	86	88	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
26	24	170	69	45	400	88	90	73000000	255000	554	1	3	3	4	2	93	79	78	89	45	57	0	0	0	0	0	0	78	83	38	84	67	89	75	63	69	88	96	91	94	89	94	76	57	79	54	77	38	30	91	79	69	79	47	53	47	15	12	12	15	9	79	79	79	86	85	85	85	86	85	85	85	85	78	78	78	85	68	62	62	62	68	64	51	51	51	64
27	27	181	76	40	626	88	89	68000000	205000	11	1	3	5	4	0	73	81	90	84	53	63	0	0	0	0	0	0	88	77	52	91	80	81	89	86	89	91	75	71	79	88	80	84	50	93	57	89	46	56	85	92	67	88	59	57	22	9	14	7	7	6	78	78	78	84	84	84	84	84	86	86	86	85	85	85	85	85	71	71	71	71	71	66	57	57	57	66
28	27	199	96	12	505	88	89	48000000	235000	226	0	4	2	1	8	69	54	58	64	56	66	85	89	72	87	49	85	14	14	13	33	12	13	19	20	35	23	46	52	61	83	45	54	68	38	70	17	23	15	13	44	27	66	20	18	16	85	89	72	85	87	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
29	32	194	85	136	224	88	88	38000000	285000	55	1	4	3	2	2	56	61	72	68	88	80	0	0	0	0	0	0	57	65	85	83	57	63	58	43	81	82	48	63	57	85	36	62	75	70	87	51	76	89	64	72	69	87	89	88	87	10	11	14	15	8	70	70	70	67	69	69	69	67	71	71	71	69	76	76	76	69	77	83	83	83	77	78	85	85	85	78
30	34	193	92	133	341	88	88	26000000	110000	226	1	3	2	1	8	69	54	58	64	56	66	88	85	69	89	53	89	12	10	10	36	12	12	12	14	34	16	51	55	47	83	36	52	78	41	71	19	25	22	12	41	23	69	25	10	13	88	85	69	89	89	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
31	33	193	92	57	226	88	88	32000000	155000	226	1	5	4	1	8	69	54	58	64	56	66	87	87	91	87	57	85	15	13	25	55	11	30	14	11	59	46	54	60	51	84	35	68	77	43	80	16	29	30	12	70	47	70	17	10	11	87	87	91	85	87	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
32	32	188	82	53	626	88	88	36000000	150000	226	0	4	1	1	8	69	54	58	64	56	66	89	82	68	91	63	84	13	10	10	50	11	10	11	10	50	34	65	62	55	86	54	51	74	41	43	14	31	27	10	30	40	65	29	10	18	89	82	68	84	91	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
33	33	173	67	136	400	88	88	36000000	265000	11	0	4	2	4	2	65	74	88	89	52	57	0	0	0	0	0	0	84	72	54	92	82	87	82	77	84	92	69	61	87	89	88	72	64	73	48	73	57	50	88	91	75	92	59	53	29	6	15	7	6	12	76	76	76	83	83	83	83	83	85	85	85	83	83	83	83	83	68	69	69	69	68	63	56	56	56	63
34	32	185	77	155	462	88	88	47000000	195000	595	1	4	4	3	0	75	86	72	79	55	83	0	0	0	0	0	0	70	88	89	78	90	79	77	76	52	81	74	76	74	91	59	88	88	91	79	79	84	54	93	77	85	80	57	48	39	12	5	13	13	10	85	85	85	80	83	83	83	80	80	80	80	79	75	75	75	79	68	66	66	66	68	66	65	65	65	66
35	33	187	78	155	341	88	88	28000000	135000	55	1	3	3	2	6	60	48	64	61	89	83	0	0	0	0	0	0	52	42	88	79	47	53	49	49	70	74	61	60	57	81	53	67	90	65	89	43	88	87	48	52	50	82	90	89	87	6	8	15	5	15	62	62	62	59	60	60	60	59	61	61	61	61	67	67	67	61	74	80	80	80	74	77	86	86	86	77
36	29	183	76	57	505	88	88	57000000	330000	155	1	4	5	3	8	45	80	90	81	70	69	0	0	0	0	0	0	88	75	58	91	82	80	86	84	92	89	50	41	60	87	71	87	30	74	73	86	60	76	75	89	73	88	72	70	62	10	11	13	7	10	75	75	75	78	79	79	79	78	81	81	81	79	85	85	85	79	75	79	79	79	75	72	69	69	69	72
37	30	180	71	57	92	88	88	56000000	170000	26	1	4	4	4	2	85	88	84	87	45	66	0	0	0	0	0	0	82	87	49	86	90	87	89	86	77	88	85	85	86	87	84	88	71	76	67	87	51	48	89	86	90	85	49	37	45	12	12	13	13	11	82	82	82	86	86	86	86	86	86	86	86	85	80	80	80	85	69	65	65	65	69	64	55	55	55	64
38	30	187	80	54	52	88	88	57000000	205000	614	1	3	4	4	7	94	85	75	80	37	69	0	0	0	0	0	0	76	89	78	77	86	79	80	74	64	82	93	95	76	87	69	82	79	78	74	79	43	48	90	77	76	85	27	25	36	6	9	15	9	9	84	84	84	83	83	83	83	83	81	81	81	81	74	74	74	81	64	58	58	58	64	61	51	51	51	61
39	27	175	69	129	389	88	88	62000000	220000	360	1	3	4	4	2	94	83	77	89	44	74	0	0	0	0	0	0	74	87	81	81	75	89	76	64	71	87	95	93	92	90	86	84	86	85	67	77	75	35	90	83	71	84	42	42	38	10	10	15	7	14	85	85	85	86	87	87	87	86	85	85	85	85	78	78	78	85	67	63	63	63	67	64	57	57	57	64
40	25	189	85	53	400	87	90	56500000	195000	67	0	2	3	2	8	64	50	71	68	88	82	0	0	0	0	0	0	58	45	86	81	40	63	61	69	80	77	61	66	59	87	60	68	78	78	85	47	79	87	54	65	44	85	88	89	88	10	11	5	14	5	65	65	65	64	66	66	66	64	67	67	67	67	74	74	74	67	78	82	82	82	78	80	85	85	85	80
41	24	173	64	121	400	87	90	64000000	210000	538	0	2	3	4	0	81	76	83	92	51	67	0	0	0	0	0	0	85	78	51	86	69	92	84	67	74	92	85	78	94	85	92	74	51	93	54	75	69	49	85	85	68	87	49	59	40	9	10	14	12	9	77	77	77	85	84	84	84	85	85	85	85	86	82	82	82	86	72	69	69	69	72	67	57	57	57	67
42	27	185	84	18	505	87	89	53500000	240000	86	1	3	3	2	6	62	72	75	72	85	89	0	0	0	0	0	0	58	64	76	83	61	69	63	74	82	78	58	66	62	86	66	86	87	88	90	81	89	86	69	77	66	84	84	87	86	13	14	16	12	12	74	74	74	71	74	74	74	71	74	74	74	72	79	79	79	72	78	84	84	84	78	79	84	84	84	79
43	26	183	78	85	626	87	88	60000000	185000	132	1	3	5	4	0	88	86	80	87	42	68	0	0	0	0	0	0	81	86	65	83	81	88	85	70	70	87	86	89	86	89	76	88	63	87	62	90	60	39	88	81	71	87	48	34	33	11	13	13	6	10	83	83	83	86	86	86	86	86	85	85	85	85	78	78	78	85	67	63	63	63	67	63	53	53	53	63
44	34	179	67	18	400	87	87	19500000	200000	86	1	3	4	3	6	66	74	79	78	84	79	0	0	0	0	0	0	68	69	66	86	74	77	70	72	83	82	68	64	69	89	77	83	78	79	76	78	87	89	70	78	61	81	87	86	80	12	11	5	13	7	74	74	74	75	76	76	76	75	78	78	78	76	81	81	81	76	80	84	84	84	80	80	82	82	82	80
45	34	183	82	18	462	87	87	18500000	135000	55	1	4	3	2	6	62	54	72	72	87	78	0	0	0	0	0	0	60	40	81	80	61	67	62	61	80	80	60	64	67	82	67	71	90	71	81	65	76	90	59	70	60	86	88	88	85	9	12	5	9	10	66	66	66	66	67	67	67	66	69	69	69	68	75	75	75	68	77	82	82	82	77	78	84	84	84	78
46	31	185	81	53	505	87	87	45000000	285000	149	1	4	4	4	7	76	82	80	86	39	77	0	0	0	0	0	0	75	84	83	85	83	86	81	73	72	90	78	74	78	85	69	82	77	79	80	76	65	39	88	86	82	87	47	24	18	13	11	5	5	7	83	83	83	83	84	84	84	83	84	84	84	82	78	78	78	82	62	62	62	62	62	58	54	54	54	58
47	32	189	86	12	626	87	87	32500000	155000	55	0	3	3	3	0	63	63	74	72	87	79	0	0	0	0	0	0	72	56	80	79	52	71	66	70	74	77	60	65	60	85	60	80	85	75	79	66	84	89	56	68	66	84	89	87	88	6	10	9	12	7	69	69	69	68	69	69	69	68	70	70	70	70	74	74	74	70	79	82	82	82	79	80	84	84	84	80
48	32	169	61	12	425	87	87	40000000	135000	149	1	3	4	4	1	88	83	80	91	35	53	0	0	0	0	0	0	79	86	40	82	70	91	82	79	74	89	93	83	94	88	92	80	59	73	39	81	59	36	87	83	79	81	25	40	40	7	10	8	10	4	78	78	78	86	85	85	85	86	85	85	85	85	78	78	78	85	65	60	60	60	65	60	46	46	46	60
49	30	191	94	57	92	87	87	41000000	130000	55	1	4	3	3	2	51	58	76	72	89	76	0	0	0	0	0	0	64	55	87	81	60	68	65	53	85	81	46	55	64	87	59	71	68	65	85	51	71	89	56	79	68	89	90	89	86	15	6	10	5	6	68	68	68	67	68	68	68	67	71	71	71	69	77	77	77	69	77	83	83	83	77	78	84	84	84	78
50	30	186	81	12	626	87	87	41000000	155000	55	1	3	3	2	8	64	55	72	66	88	79	0	0	0	0	0	0	64	45	82	79	38	62	63	69	85	75	61	66	59	87	50	78	84	78	79	58	81	88	54	62	58	86	90	90	86	16	6	14	16	14	66	66	66	64	66	66	66	64	67	67	67	67	74	74	74	67	78	83	83	83	78	80	84	84	84	80
51	30	170	68	136	224	87	87	40000000	240000	227	0	3	3	3	2	90	69	81	83	79	73	0	0	0	0	0	0	87	73	70	84	60	81	80	63	79	85	91	90	89	85	85	64	81	92	61	66	77	83	83	78	59	82	74	83	84	13	15	13	6	13	76	76	76	82	80	80	80	82	81	81	81	83	81	81	81	83	84	81	81	81	84	84	78	78	78	84
52	28	174	70	136	226	87	87	50000000	180000	157	1	3	3	5	8	69	74	85	90	69	63	0	0	0	0	0	0	78	69	54	90	86	90	85	77	87	91	76	64	91	86	88	76	76	75	59	79	56	83	79	86	75	87	67	67	71	6	11	7	9	13	75	75	75	82	81	81	81	82	84	84	84	82	84	84	84	82	78	78	78	78	78	75	69	69	69	75
53	32	185	80	33	505	87	87	30500000	195000	226	1	3	3	1	8	69	54	58	64	56	66	90	81	75	90	53	82	11	15	11	30	11	16	11	15	37	19	54	53	60	84	61	56	74	39	75	13	32	20	16	68	25	67	28	14	14	90	81	75	82	90	57	57	57	59	58	58	58	59	58	58	58	59	57	57	57	59	56	56	56	56	56	56	55	55	55	56
54	28	163	59	77	425	87	87	52000000	140000	139	1	3	4	4	2	89	79	85	91	35	47	0	0	0	0	0	0	86	77	59	87	74	90	87	77	78	93	94	85	94	84	93	79	52	72	40	85	34	26	85	89	68	83	49	24	22	8	4	14	9	10	79	79	79	87	86	86	86	87	87	87	87	86	79	79	79	86	63	58	58	58	63	58	44	44	44	58

Model Building

Scaling(StandardScaler)

from sklearn import preprocessing
# import module
from sklearn.preprocessing import StandardScaler

x = data_final.values # numpy array
scaler = StandardScaler()
x_scaled = scaler.fit_transform(x)
X_norm = pd.DataFrame(x_scaled)

Principal component Analysis

from sklearn.decomposition import PCA

pca = PCA(n_components = 2) # 2D PCA for the plot
X_ = pd.DataFrame(pca.fit_transform(X_norm))

X_ .columns = ['x', 'y']
X_ 

	x	y
0	-7.941322	-5.268978
1	-6.240008	-4.514799
2	-6.613192	-6.118575
3	13.210379	-3.710364
4	-6.211079	-5.065423
5	-6.673138	-0.034588
6	12.769798	-3.361838
7	0.492448	6.602990
8	-5.933785	1.731451
9	-5.494400	-3.054408
10	-5.035945	-4.338662
11	5.590876	7.657040
12	-4.032691	-1.709355
13	13.294981	-3.701327
14	12.654167	-3.487976
15	-2.889708	5.174976
16	4.899882	7.639632
17	-4.557110	-4.915256
18	-1.865040	6.298640
19	-3.992588	-1.500011
20	-3.703487	-2.837978
21	-1.283103	6.092856
22	-5.969818	-0.905028
23	-4.404356	-2.886814
24	-4.921390	2.128812
25	12.426179	-3.652362
26	-3.960186	-4.331479
27	-4.968119	-1.541618
28	13.837686	-3.250089
29	0.914014	6.844958
30	14.239122	-2.656339
31	12.697395	-3.014220
32	13.378240	-3.181999
33	-3.939597	-1.375695
34	-3.069954	-0.405504
35	3.921501	7.811080
36	-3.382784	2.260060
37	-4.790843	-2.630740
38	-2.855089	-3.746151
39	-4.710209	-2.931791
40	1.654499	6.950502
41	-4.586942	-1.934865
42	-1.140819	6.053115
43	-4.499649	-3.201746
44	-2.529125	5.252475
45	1.007402	6.813163
46	-3.318703	-2.438586
47	0.159826	6.569679
48	-4.173170	-4.624249
49	0.521369	6.682640
50	1.411233	7.024332
51	-3.946315	3.605950
52	-4.238518	1.821699
53	13.242395	-3.190847
54	-4.451209	-5.496386

Elbow method

#elbow method
from sklearn.cluster import KMeans
wcss=[]
for i in range (1,11):
    kmeans=KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=42)
    kmeans.fit(X_)
    wcss.append(kmeans.inertia_)
plt.plot(range(1,11),wcss,marker="1")
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()

Fitting K-Means Clustering to the dataset

kmeans = KMeans(n_clusters = 3, random_state = 42)
y_kmeans = kmeans.fit_predict(X_)
print(y_kmeans)

[0 0 0 2 0 0 2 1 0 0 0 1 0 2 2 1 1 0 1 0 0 1 0 0 0 2 0 0 2 1 2 2 2 0 0 1 0
 0 0 0 1 0 1 0 1 1 0 1 0 1 1 1 0 2 0]

test=kmeans.predict(np.asarray([[4.383253,-0.978393]]))
test[0]

1

label=kmeans.labels_
label

array([0, 0, 0, 2, 0, 0, 2, 1, 0, 0, 0, 1, 0, 2, 2, 1, 1, 0, 1, 0, 0, 1,
       0, 0, 0, 2, 0, 0, 2, 1, 2, 2, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0,
       1, 1, 0, 1, 0, 1, 1, 1, 0, 2, 0])

from sklearn.metrics import silhouette_score
score=silhouette_score(X_,label)
score

0.7143321481575283

# centroid values
centroid = kmeans.cluster_centers_

# cluster values
clusters = kmeans.labels_.tolist()

X_['cluster'] = clusters
X_['name'] = names
X_['team_position']= team_positions
X_.head()

	x	y	cluster	name	team_position
0	-7.941322	-5.268978	0	L. Messi	RW
1	-6.240008	-4.514799	0	Cristiano Ronaldo	LW
2	-6.613192	-6.118575	0	Neymar Jr	CAM
3	13.210379	-3.710364	2	J. Oblak	GK
4	-6.211079	-5.065423	0	E. Hazard	LW

#attack = ['LW', 'RW', 'ST', 'LF', 'RF', 'CF', 'LS', 'RS']
#defence = ['LWB', 'RWB', 'CB', 'LB', 'RB', 'LCB', 'RCB']
#Midfield = ['CM', 'RCM', 'LCM', 'RM', 'LM', 'CAM', 'RDM', 'LDM', 'CDM', 'RAM', 'LAM']

Players in the first cluster

X_[y_kmeans==0]

	x	y	cluster	name	team_position
0	-7.941322	-5.268978	0	L. Messi	RW
1	-6.240008	-4.514799	0	Cristiano Ronaldo	LW
2	-6.613192	-6.118575	0	Neymar Jr	CAM
4	-6.211079	-5.065423	0	E. Hazard	LW
5	-6.673138	-0.034588	0	K. De Bruyne	RCM
8	-5.933785	1.731451	0	L. Modrić	RCM
9	-5.494400	-3.054408	0	M. Salah	RW
10	-5.035945	-4.338662	0	K. Mbappé	RW
12	-4.032691	-1.709355	0	H. Kane	ST
17	-4.557110	-4.915256	0	S. Agüero	ST
19	-3.992588	-1.500011	0	L. Suárez	ST
20	-3.703487	-2.837978	0	R. Lewandowski	ST
22	-5.969818	-0.905028	0	A. Griezmann	LW
23	-4.404356	-2.886814	0	P. Dybala	RW
24	-4.921390	2.128812	0	P. Pogba	LDM
26	-3.960186	-4.331479	0	R. Sterling	LW
27	-4.968119	-1.541618	0	C. Eriksen	RM
33	-3.939597	-1.375695	0	David Silva	LCM
34	-3.069954	-0.405504	0	E. Cavani	ST
36	-3.382784	2.260060	0	T. Kroos	LCM
37	-4.790843	-2.630740	0	M. Reus	CAM
38	-2.855089	-3.746151	0	P. Aubameyang	LM
39	-4.710209	-2.931791	0	S. Mané	LW
41	-4.586942	-1.934865	0	Bernardo Silva	RW
43	-4.499649	-3.201746	0	H. Son	LM
46	-3.318703	-2.438586	0	K. Benzema	CF
48	-4.173170	-4.624249	0	D. Mertens	SUB
52	-4.238518	1.821699	0	Thiago	CDM
54	-4.451209	-5.496386	0	L. Insigne	LS

In the first cluster we have attcakers and Midfielders

Players in the second cluster

X_[y_kmeans==1]

	x	y	cluster	name	team_position
7	0.492448	6.602990	1	V. van Dijk	LCB
11	5.590876	7.657040	1	K. Koulibaly	LCB
15	-2.889708	5.174976	1	N. Kanté	RCM
16	4.899882	7.639632	1	G. Chiellini	LCB
18	-1.865040	6.298640	1	Sergio Ramos	LCB
21	-1.283103	6.092856	1	Sergio Busquets	CDM
29	0.914014	6.844958	1	Piqué	RCB
35	3.921501	7.811080	1	D. Godín	RCB
40	1.654499	6.950502	1	A. Laporte	LCB
42	-1.140819	6.053115	1	Casemiro	CDM
44	-2.529125	5.252475	1	Fernandinho	CDM
45	1.007402	6.813163	1	Thiago Silva	LCB
47	0.159826	6.569679	1	J. Vertonghen	LCB
49	0.521369	6.682640	1	M. Hummels	LCB
50	1.411233	7.024332	1	T. Alderweireld	RCB
51	-3.946315	3.605950	1	Jordi Alba	LB

In the second cluster we have Goal keepers

Players in the third cluster

X_[y_kmeans==2]

	x	y	cluster	name	team_position
3	13.210379	-3.710364	2	J. Oblak	GK
6	12.769798	-3.361838	2	M. ter Stegen	GK
13	13.294981	-3.701327	2	Alisson	GK
14	12.654167	-3.487976	2	De Gea	GK
25	12.426179	-3.652362	2	Ederson	GK
28	13.837686	-3.250089	2	T. Courtois	GK
30	14.239122	-2.656339	2	S. Handanovič	GK
31	12.697395	-3.014220	2	M. Neuer	GK
32	13.378240	-3.181999	2	H. Lloris	GK
53	13.242395	-3.190847	2	K. Navas	SUB

In the third cluster we have Defenders and Midfielders

Visualisation of the clustering

# Visualising the clusters
plt.figure(figsize=(10,5))
plt.scatter(X_[y_kmeans == 0]['x'], X_[y_kmeans == 0]['y'],marker='*',
            s = 100, c = 'red', label = 'attcakers and Midfielders')
plt.scatter(X_[y_kmeans == 1]['x'], X_[y_kmeans == 1]['y'],marker='$...$',
            s = 600, c = 'blue', label = 'goal keepers')
plt.scatter(X_[y_kmeans == 2]['x'], X_[y_kmeans == 2]['y'],marker="1",
            s = 200, c = 'tomato', label = 'Defender and Midfielders')

plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 250, 
            c = 'yellow',label = 'Centroids')
plt.title('Clusters of Players',fontsize=20)
plt.xlabel('X')
plt.ylabel('Y')
plt.legend()
plt.show()

Conculsion:

In this project, we will be looking at the player data provided by FIFA which contains information such as personal details, wages, physical attributes, technical skills, potential and their positional strengths. This is primarily data of FIFA 20. Through this project, we got a glimpse of insights behind the beautiful game and the kind of information and decisions a football manager goes through.

By using FIFA 20 datadset, intially we analysed the players traits and their differents skills through the visualisation and after ploted ploar charts on the basis of different player positions and their unique skills. Finally, we applied a Dimensionality- reuction method called Principal Component analysis(PCA) for reducing columns into 2 dimensions for the fitting of k-means clustering algorithm and after we given k values as 3 for 3 clusters. Through the above k-means clustering, we get silhouette_score is 0.71, it mean that we clustered very well.

In the first cluster we have Attcakers and Midfielders.

In the second cluster we have Goal keepers and,

In the third cluster we have Defenders and Midfielders

FIFA is a virtual game, here a player concentration on how to kick the ball into the opponent zone and how to stop opponent kicked ball in his zone.